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The paper addresses the issue of determining the parameter values of the agent-based model of social campaign message adoption relying on Bass's classical innovation diffusion model. The problem concerns the parameters that reflect the influence of advertising and social communication on the adoption of the idea conveyed in the social campaign. Due to the fact that the factors influencing the behavior of message recipients are conditioned by their personality, circumstances or reaction to the environment, the conventional methods for estimating these parameters may not deliver a model reproducing reality with the required accuracy. Therefore, this paper proposes a procedure for determining the values of agent-based model parameters that relies on an experimental data acquisition procedure using a combination of cognitive neuroscience techniques and a survey method. The presented research examines a social campaign promoting road safety. The obtained results prove the suitability of the suggested solution for estimating the parameters of the agent-based model of social campaign message adoption. The proposed approach contributes to the methodology of data collection and parameter estimation in building agent-based models, although it is not without some limitations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive mental processes underlie the connection between dreams and creativity and is shown to be validated with the existing cognitive neuroscience literature. [ABSTRACT FROM AUTHOR]

The primary objective of the paper is to present the essence of the new paradigm in the management science, which is neuroentrepreneurship. This objective determined the paper's layout. In the first part, the theories of entrepreneurship are presented, including the context of research on entrepreneurship. In the second part, the techniques of cognitive neuroscience are described, which are used to determine the impact of thought processes on the recognition and use of a business opportunity by an entrepreneur. This part also analyses the new paradigm of management sciences, i.e. neuroentrepreneurship. In this respect, the most important achievements of science in diagnosing the impact of neuronal impulses on the entrepreneurial process are presented. [ABSTRACT FROM AUTHOR]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

Important questions have arisen about the capacity of traditional questioned document methodology to differentiate between genuine and forged exemplars of an author's handwriting. This paper does not address that dispute. The paper describes the first part of a research project investigating whether imitators (forgers) can reliably differentiate between genuine samples and forgeries. This paper takes a different, cognitive neuroscience approach and investigates the overlooked topic of the mental processing of forgeries by forgers. The paper tried to examine the neural mechanisms of imitators' (forgers') attentive processing of forged and genuine exemplars. The data in this initial phase of the study showed imitators experienced more difficulty evaluating their own forgeries perhaps because the forgeries included both the features they had consciously copied and some of their own handwriting characteristics that they could not completely suppress. A subsequent phase of the study will use self-reporting and eye movement tracking studies, in this phase we shall attempt to identify the specific types of features the imitators relied on in correctly classifying the exhibit as a forgery. We shall then enlist the services of experienced questioned document examiners endeavor to determine whether those characteristics appear in genuine exemplars of the forgers' handwriting. The identification of those categories of features may hold the potential for improving both the detection of forgeries and the identification of the forger. [ABSTRACT FROM AUTHOR]

In this paper, joint decision making processes are studied and the role of cognitive metaphors as mental models in them. A second-order self-modeling network model is introduced based on mechanisms known from cognitive and social neuroscience and cognitive metaphor and mental model literature. The cognitive metaphors were modeled as specific forms of mental models providing a form of modulation within the joint decision making process. The model addresses not only the use of these mental models in the decision making, but also their hebbian learning and the control over the learning. The obtained self-modeling network model was applied to two types of metaphors that affect joint decision making in different manners: a cooperative metaphor and a competitive metaphor. By a number of scenarios it was shown how the obtained self-modeling network model can be used to simulate and analyze joint decision processes and how they are influenced by such cognitive metaphors. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience techniques have become a great complement to traditional methods in marketing research. However, many difficulties have arisen, such as the collection of reliable data, processing and proper analysis. The paper presents good practices of collecting neurophysiological data for the evaluation of media messages in social campaigns. For this purpose, the most common difficulties during signal recording for devices such as EEG, GSR, HR, Eye Tracker were identified. The problems that arose were assigned to three main groups that could be affected, i.e.the examing person, the examing enviroment and the examing equipment. In the final stage, solutions were proposed to reduce or eliminate them. [ABSTRACT FROM AUTHOR]

Abstract The task of the daily box office prediction model is to build a dynamic prediction model to rolling forecast daily box office. It is a complex task as the movie box office has a short life cycle, and the static data and dynamic data that affect the trend of box office are heterogeneous. This paper proposes an end-to-end deep learning model for daily box office prediction, called Deep-DBP which consists of temporal component and static characteristics component. The temporal component is the main component which uses LSTM to learn the temporal dependencies between data points. The static characteristics component is an auxiliary component and it integrates static characteristics to improve prediction effect. The Deep-DBP can overcome the problems that the ARIMA and traditional ANN model cannot solve. The structure of input and output proposed in the model can well handle short time series prediction problem. It is a successful case in dealing with multi-source and multi-view data, addition of static characteristics component reduces the prediction error by 7%. The prediction error of Deep-DBP is 30.1%, which is better than that of the previous model. The experiment proved that the more training data collected, the better the prediction effect. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a fast and reliable approach for the localization of an iris image using neural networks by comparing some of the existing localization methods. Normally any biometric recognition system is used for identification of an individual and verified with the available database to check whether the person is authorized or not. Nowadays, iris recognition systems are considered the best authentication method compared to other biometric systems due to the unique characteristic feature of the human iris. In iris recognition systems, the important and difficult step is to locate or segment the iris from the input eye image which is responsible for success rate of the iris recognition. Hence this paper suggests a efficient approach to locate the iris using neural networks in order to improve the efficiency of recognition systems. Further, the paper compares the existing iris localization methods such as Daugman algorithm, Hough transform and Canny edge detector algorithm. The best localization algorithm is chosen for training a network and simulating for efficient and fast segmentation of irises with good success rate. [Copyright &y& Elsevier]

This paper presents selected findings of doctoral research exploring how design could be used to influence user behaviour towards more sustainable practices. It describes three strategies for changing user behaviour through design drawn from literature and outlines the methodology and findings of a case study exploring the application of these strategies in sustainable design. Drawing on the perceptions of design professionals interviewed in response to one of the concepts generated, the paper goes on to explore the perceived acceptability and effectiveness of these strategies. It concludes by commenting on the wider implications of these perceptions for ongoing research. [Copyright &y& Elsevier]

The paper presents a pilot interdisciplinary research study carried out as a step towards understanding the neurological basis of design thinking. The study involved functional magnetic resonance imaging (fMRI) of volunteers while performing design and problem-solving tasks. The findings suggest that design and problem solving involve distinct cognitive functions associated with distinct brain networks. The paper introduces the methodology, presents the findings, and discusses the potential role of brain imaging in design research. [Copyright &y& Elsevier]

Abstract: Booming sound is one of the important sounds in a passenger car. The aim of the paper is to develop the objective evaluation method of interior booming sound. The development method is based on the sound metrics and ANN (artificial neural network). The developed method is called the booming index. Previous work maintained that booming sound quality is related to loudness and sharpness – the sound metrics used in psychoacoustics – and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20Hz and 20kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200Hz. The relationship between the booming index and sound metric is identified by an ANN. The updated booming index has been successfully applied to the objective evaluation of the booming sound quality of mass-produced passenger cars. [Copyright &y& Elsevier]

Abstract: This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using an Evolutionary Algorithm. Only the latest module added to the network is trained, the previous structure remains fixed. The algorithm allows information from different data domains to be integrated into the network and because the search space in each iteration is small, large and complex networks with a modular structure can emerge naturally. The paper describes an application of the algorithm to a legged robot and discusses its biological inspiration. [Copyright &y& Elsevier]

Abstract: This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detailed digital elevation models (DEMs) for all land masses between 60°N and 57°S. Exploiting these data for recognition and extraction of geomorphometric features is a challenging task. Results obtained with two methods, Wood''s morphometric parameterization and the Self Organizing Map (SOM), are presented in this paper. Four morphometric parameters (slope, minimum curvature, maximum curvature and cross-sectional curvature) were derived by fitting a bivariate quadratic surface with a window size of 5 by 5 to the SRTM DEM. These parameters were then used as input to the two methods. Wood''s morphometric parameterization provides point-based features (peak, pit and pass), line-based features (channel and ridge) and area-based features (planar). Since point-based features are defined as having a very small slope when their neighbors are considered, two tolerance values (slope tolerance and curvature tolerance) are introduced. Selection of suitable values for the tolerance parameters is crucial for obtaining useful results. The SOM as an unsupervised neural network algorithm is employed for the classification of the same morphometric parameters into ten classes characterized by morphometric position (crest, channel, ridge and plan area) subdivided by slope ranges. These terrain features are generic landform element and can be used to improve mapping and modeling of soils, vegetation, and land use, as well as ecological, hydrological and geomorphological features. These landform elements are the smallest homogeneous divisions of the land surface at the given resolution. The result showed that the SOM is an efficient scalable tool for analyzing geomorphometric features as meaningful landform elements, and uses the full potential of morphometric characteristics. [Copyright &y& Elsevier]

Abstract: In this paper, cellular neural networks with complex deviating arguments are considered. Sufficient conditions for the existence of the periodic solutions are established by using the coincidence degree theorem. The results of this paper are new and complement previously known results. [Copyright &y& Elsevier]

Abstract: In this paper the shunting inhibitory cellular neural networks (SICNNs) with time-varying delays are considered. Without assuming the global Lipschitz and bounded conditions of activation functions, sufficient conditions for the existence of the almost periodic solutions are established by using a fixed point theorem. The results of this paper are new and complement previously known results. [Copyright &y& Elsevier]

Abstract: Based on a scope of 10,120 articles on ANNs, this paper uses data mining including association rules and cluster analysis, to survey these ANNs papers through keyword classification and clustering of articles from 1995 to 2005, exploring the ANNs methodologies and application developments during that period. The four decision variables of keywords, author’s nationality, research category, and year of publication, are implemented for data mining with total of 110,080 data items. The research findings show that some specific ANNs methodologies and applications pattern can be extracted from the mining results, and these describe the ANNs development over this period. In addition, using more data mining approaches for analysis could provide different explanations for ANNs development. Finally, discussion and brief conclusion are presented. [Copyright &y& Elsevier]

Abstract: While most of the existing artificial neural networks (ANN) models for building energy prediction are static in nature, this paper evaluates the performance of adaptive ANN models that are capable of adapting themselves to unexpected pattern changes in the incoming data, and therefore can be used for the real-time on-line building energy prediction. Two adaptive ANN models are proposed and tested: accumulative training and sliding window training. The computational experiments presented in the paper use both simulated (synthetic) data and measured data. In the case of synthetic data, the accumulative training technique appears to have an almost equal performance with the sliding window training approach, in terms of training time and accuracy. In the case of real measurements, the sliding window technique gives better results, compared with the accumulative training, if the coefficient of variance is used as an indicator. [Copyright &y& Elsevier]

Abstract Episodic memory is a critical component of any computational representation of cognition. While declarative and procedural memory have been extensively studied by the cognitive modeling community, episodic memory has only recently been considered as an important component of cognitive architecture (CA) development. Human neurological evidence supports the concept that memory is stored in the mind in different forms and locations, with episodic memory being critical for learning temporal sequences of events and associating context to learned information. Recent neurological evidence supports the idea that episodic memory is distinct from semantic memory and procedural memory. This paper reviews the current research on episodic memory in neurology and CAs and argues for its inclusion in the Standard Model of Mind. [ABSTRACT FROM AUTHOR]

This special issue of Cognitive Systems Research features the top twelve papers from the 4th International Conference on Cognitive Modeling (ICCM-2001). These papers represent the current trends in computational cognitive modeling, displaying both the diversity and commonalities of the field. This introduction to the special issue describes the motivations for the conference and the special issue, and overviews the diversity and commonalities found in the papers. [Copyright &y& Elsevier]

Abstract: Objective: To assess executive function in children with developmental dyspraxia. Patients and method: Inclusion criteria: children aged 8years to 12years 5months at the time of the study, diagnosed with developmental dyspraxia between January, 2008 and August, 2009 by a multidisciplinary team in one single center. Assessment tools: (1) Paper-and-pencil neuropsychological and ecological tests to assess flexibility, planning, inhibition and prospective memory; (2) two questionnaires answered by parents; (3) the ‘Children's Cooking Task’ (CCT), an ecological task performed in a real environment (Chevignard et al., 2009 [15]). In this last test, children were compared to matched controls. Non-parametric statistical tests were used. Results: Thirteen patients participated in the study (11 boys–2 girls; mean age 10.3years [SD=1.3]). Neuropsychological tests highlighted planning and inhibition disorders, but no impaired flexibility. For more than half of the children, the questionnaires indicated impaired executive functions in daily life tasks. Finally, patients showed a significantly increased rate of errors during the CCT, compared with the control group (P <0.001). Conclusion: Overall results suggest that some children diagnosed with developmental dyspraxia also exhibit executive function disorders. Ecological tests seem more sensitive for identifying executive function disorders than conventional tests. [Copyright &y& Elsevier]

Abstract: Despite the fact that there are now a large number of successful bio-inspired applications in use in science and technology, we are still quite far removed from creating applications that display human-like intelligence. Putting together successful bio-inspired applications remains something of a black art; this is due to a lack of fundamental understanding of brain function. The causes for these problems were analysed in a ’Roadmap for Neuro-IT’ and were deemed to be sufficiently pressing to motivate one of five ’Grand Challenges’ in Neuro-IT: the ’Constructed Brain’. The challenge argued that one of the main bottlenecks to progress is that data taking and modelling in the neurosciences are being fractured across many research groups and communities; it makes proposals for addressing the issue. Similar observations, raised in two OECD workgroup papers have led to the formation of the International Neuroinformatics Coordinating Facility. As a consequence we can conclude that there is now a much higher awareness of the problems and that in the neurosciences the situation has improved dramatically. I will review recent initiatives to facilitate data management, modelling and simulation in the neurosciences. One problem remains unaddressed, however. The project-based funding of the brain sciences sets an upper limit to the complexity of brain models. Since the brain is truly complex, any individual project will fall short of capturing the brain’s complexity. The creation of a central infrastructure for the brain sciences is inescapable, but is unlikely to be realised soon. I will outline suggestions to handle the current situation. [Copyright &y& Elsevier]

Abstract: Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs. [Copyright &y& Elsevier]

We have developed a computational design tool to help designers create conceptual solutions to detailed functional specifications. The computational method extracts design knowledge from an expanding online design library in the form of procedural rules, and provides these rules as the building blocks for solving new problems. In this paper, we study how this automated approach would benefit designers during concept generation. Accordingly, we test the effects of using our computational tool as an aid for concept generation in an experiment mimicking real design scenarios. Three metrics (completeness, novelty and variety) are used to evaluate the solutions generated to two separate design problems in order to determine how effective the computational method outputs are in improving conceptual design generation. [Copyright &y& Elsevier]

Abstract: Turbulent forced convection correlations are documented in the literature for air, gases and vapors (), for common liquids () and for liquid metals (). In spite of this, there is a small gap in the Pr sub-interval between 0.1 and 1.0, which is occupied by binary gas mixtures. In this paper, data for turbulent forced convection for the in-tube flow have been gathered and a fully connected back-propagation Artificial Neural Network (ANN) is used to learn the pattern of Nu as a double-valued function of Re and Pr. The available data are separated in two subsets to train and test the neural network. A set with 80% of the data is used to train the ANN and the remaining 20% are used for testing. After the neural network is trained, we make use of the excellent nonlinear interpolation capabilities of ANNs to predict Nu for the sought range for Re between 104 and 106. These predictions are later extended to generate a comprehensive correlation for Re between 104 and 106 that aptly covers the complete spectrum of Prandtl numbers. [Copyright &y& Elsevier]

Abstract: In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems. [Copyright &y& Elsevier]

This paper presents a method for automatic real time face and mouth recognition using radial basis function neural networks (RBFNN). The proposed method uses the motion information to localize the face region, and the face region is processed in color space to determine the locations of the eyes. The center of the mouth is determined relative to the locations of the eyes. Facial and mouth features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes, and mouth features are extracted relative to the locations of the eyes and mouth. The facial and mouth features are given as input to radial basis function neural networks. The RBFNN is used to recognize a person in video sequences using face and mouth modalities. The evidence from face and mouth modalities are combined using a weighting rule, and the result is used for identification and authentication. The performance of the system using facial and mouth features is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.73% for 50 subjects. The performance of the system is also evaluated for XM2VTS database, and the system achieves a recognition rate (RR) of 100% an equal error rate (EER) of about 0.25% for 50 subjects. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the existence and exponential stability of positive almost periodic solutions of high-order Hopfield neural networks (HHNNs) with time-varying delays. Some sufficient conditions for the existence and exponential stability of positive almost periodic solutions are established. [Copyright &y& Elsevier]

Abstract: The stability of stochastic delayed Hopfield neural networks (DHNN) is investigated in this paper. Under the help of suitable Lyapunov function and the semimartingale convergence theorem, we obtain some sufficient criteria to check the almost sure exponential stability of the DHNN. [Copyright &y& Elsevier]

Abstract: This paper considers the possibility of using artificial neural network models to identify model for swelling behavior as new techniques. Multi-layer feed-forward, radial basis function and generalized regression neural network models were employed to predict the swelling behaviors of -alginate hydrogels under different environmental conditions of pH and temperature. The results show that an excellent correlation between the experimental and predicted swelling ratios was obtained by the artificial neural networks. Generalized regression neural network has a better performance than the other neural network models. The absolute mean error, the determination coefficient and the standard error of prediction were used as performance criteria. In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology. [Copyright &y& Elsevier]

Abstract: This paper describes an approach for pattern recognition using genetic algorithm and general regression neural network (GRNN). The designed system can be used for both 3D object recognition from 2D poses of the object and handwritten digit recognition applications. The system does not require any preprocessing and feature extraction stage before the recognition. In GRNN, placement of centers has significant effect on the performance of the network. The centers and widths of the hidden layer neuron basis functions are coded in a chromosome and these two critical parameters are determined by the optimization using genetic algorithms. Experimental results show that the optimized GRNN provides higher recognition ability compared with that of unoptimized GRNN. [Copyright &y& Elsevier]

Abstract: Due to the implicit characteristics of learning disabilities (LD), the identification or diagnosis of students with learning disabilities has long been a difficult issue. The LD diagnosis procedure usually involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In this paper, we apply two well-known artificial intelligence techniques, artificial neural network (ANN) and support vector machine (SVM), to the LD diagnosis problem. To improve the overall identification accuracy, we also experiment with GA-based feature selection algorithms as the pre-processing step. To the best of our knowledge, this is the first attempt in applying ANN or SVM to similar application. The experimental results show that ANN in general performs better than SVM in this application, and the wrapper-based GA feature selection procedure can improve the LD identification accuracy, and among all, the combination of using SVM learner in the feature selection procedure and ANN learner in the classification stage results in feature set that achieves the best prediction accuracy. Most important of all, the study indicates that the ANN classifier can correctly identify up to 50% of the LD students with 100% confidence, which is much better than currently used LD diagnosis predictors derived through the statistical method. Consequently, a properly trained ANN classification model can be a strong predictor for use in the LD diagnosis procedure. Furthermore, a well-trained ANN model can also be used to verify whether a LD diagnosis procedure is adequate. In conclusion, we expect that AI techniques like ANN or SVM will certainly play an essential role in future LD diagnosis applications. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach using Artificial Neural Networks (ANNs) models to simulate the response during nanohardness tests of a variety of materials with nonlinear behavior. The ANNs continuous input and output variables usually include material parameters, indentation deflection, and resisting force. Different ANN models, including dimensionless input/output variables, are generated and trained with discrete finite-element (FE) simulations with different geometries and nonlinear material parameters. Only the monotonic loading part of the load–displacement indentation response is used to generate the trained ANN models. This is a departure from classical indentation simulations or tests where typically the unloading portion is used to determine the stiffness and hardness. The experimental part of this study includes nanoindentation tests performed on a silicon (Si) substrate with and without a nanocrystalline copper (Cu) film. The new ANN models are used to back-calculate (inverse problem) the in situ nonlinear material parameters for different copper material systems. The results are compared with available data in the literature. The proposed FE–ANN modeling approach is very effective and can be used in calibrating and predicting the in situ inelastic material properties using the monotonic part of the indentation response and for depths above 50nm where the overall resisting force represents a continuum response. [Copyright &y& Elsevier]

Abstract: Positioning a user is an essential ingredient of a location-based system. For the outdoor positioning, GPS is practically used. For the indoor positioning, Active Badge, BAT, Cricket, and so on have been introduced. These methods are very accurate but require special equipments dedicated for positioning. Instead of using special equipments, using existing equipments is more economical. For this reason, positioning methods of using existing wireless LAN access points have recently been introduced. Among the methods employed by them, the fingerprint methods are the most promising. Probabilistic method, K-NN (Nearest Neighbor), and Neural networks are the techniques used by the most location fingerprinting. We are proposing a new technique which is more efficient than these three. Our technique builds a decision tree during the off-line phase and determines a user’s location referring to the tree. Time complexity analysis and experimental accuracy analysis of the proposed technique are presented in this paper. [Copyright &y& Elsevier]

Abstract: It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are developed from equivolume charting. This paper explores the profitability of stock trading by using a neural network model developed to assist the trading decisions of the VAMA and EMV. The generalized regression neural network (GRNN) is chosen and utilized on past S&P 500 index data. For the VAMA, the GRNN is used to predict the future stock prices, as well as the future width size of the equivolume boxes typically utilized on an equivolume chart, for calculating the future value of the VAMA. For the EMV, the GRNN is also used to predict the future value of the EMV. The idea is to further exploit the equivolume potential by using a forecasting system to predict the future equivolume measurements, allowing investors to enter or exit trades earlier. The results show that the stock trading using the neural network with the VAMA and EMV outperforms the results of stock trading generated from the VAMA and EMV without neural network assistance, the simple moving averages (MA) in isolation, and the buy-and-hold trading strategy. [Copyright &y& Elsevier]

Abstract: This paper presents an approach to automatically recognize hand geometry pattern based on a database. The system does not require any feature extraction stage before the identification. General regression neural networks are used for the classification and/or verification of the patterns. Simulation results show that hand geometry pattern identification by the proposed method improves the identification rate considerably. To show the system performance, false acceptance ratio and false rejection ratio are given. [Copyright &y& Elsevier]

Abstract: Fault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. Improved data validation has resulted in more stable processes and better quality of the products. In this paper, an on-line fault detection and isolation system consisting of a combination of principal component analysis (PCA) and two neural networks (NNs), radial basis function network (RBFN) and self-organizing map (SOM), is presented. The system detects and isolates faulty operation of the analyzers in an ethylene cracking furnace. The test results with real-time process data are presented and discussed. [Copyright &y& Elsevier]

Abstract: In a curtain-wall system, the main and the most possible cause of failures, is the total or partial destruction of its connections with the bearing structure. The present paper deals with the respective health monitoring problem and proposes an Artificial Neural Network (ANN) in order to identify possible imperfections in a typical curtain-wall system. Several Finite Element (FE) models of the curtain-wall system were developed and a parametric analysis was carried out dealing with the loss of rigidity in the aforementioned connections. During the numerical investigations, datasets containing the deflections of the columns of the curtain-wall structure were computed. The obtained results were used to create the Patterns Database, which, in turn, was used as the input for the training of the ANNs. Due to the relatively small number of training patterns, the regularization technique was also employed in order to improve the network generalization. The number of sensors and their optimal placement for appropriate network training were investigated. A wide variety of network architectures was studied and their influence on the network training was analyzed. The obtained results showed that ANNs can be an efficient method for the identification and localization of imperfections in curtain-wall systems. [Copyright &y& Elsevier]

Abstract: Safe use of low lying and densely populated coastal regions depends critically on the performance of coastal structures in defending these areas against storm surges, wave attack, flooding and erosion. Continuing sea level rise and climate change (storms are becoming rougher) emphasise the need for reliable and robust predictions as higher storm surges and bigger storms may lead to flooding. Population pressures on land use in coastal regions have sometimes ignored age-old appreciation of coastal hazards. The CLASH research project EVK3-CT-2001-00058 was funded by the EU to provide “Crest Level Assessment of coastal Structures by full scale monitoring, neural network prediction and Hazard analysis on permissible wave overtopping”. One of its main objectives was to produce a generally applicable prediction method based on permissible wave overtopping and hazard analysis. This paper describes the problems related to wave overtopping and crest level design of coastal structures. Within the CLASH background, the development of a generic prediction method and its use taking into account hazards is presented. [Copyright &y& Elsevier]

Abstract: In this paper, a new neural network (NN) based formula is proposed for the determination of tensile strength of basalt in terms of ultrasonic pulse velocity, dry density and water absorption parameters. The data used in training and testing NN are obtained from an extensive experimental work carried out on 86 samples that were cored from basalt blocks collected from the southeast of Turkey. The NN-based results were compared with the experimental results and the proposed NN-based formula was found to be practical in predicting the tensile strength of basalt. [Copyright &y& Elsevier]

Abstract: The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress–strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature–displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies. [Copyright &y& Elsevier]

Abstract: The 486-m long roof structure of Shenzhen Citizens’ Centre is the world’s longest spatial lattice structure. This paper presents some selected results from a combined wind tunnel and numerical simulation study of wind effects on the extra-long-span roof structure. In this study, simultaneous pressure measurements on its entire roof are made in a boundary layer wind tunnel, and the measured wind pressures, such as mean, root-mean-square (rms) and peak pressure coefficient distributions on the roof are presented and discussed. Based on the measured data from a number of pressure taps, a numerical simulation approach using backpropagation neural networks (BPNN) is developed for the predictions of wind-induced pressure time series at other roof locations which are not covered in the wind tunnel measurements. The BPNN is trained with the pressure data time series measured from adjacent pressure taps. The good performance of the developed neural network is demonstrated by comparing the predictions with the model test results, illustrating that the BPNN approach can serve as an effective tool for the design and analysis of wind effects on large roof structures in conjunction with wind tunnel tests. [Copyright &y& Elsevier]

Abstract: In this paper recurrent neural networks with time-varying delays and continuously distributed delays are considered. Without assuming the global Lipschitz conditions of activation functions, some sufficient conditions for the existence and local exponential stability of the almost periodic solutions are established, which are new and complement previously known results. [Copyright &y& Elsevier]

Abstract: A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-means+HLA). Our proposed method has the best generalization performance. [Copyright &y& Elsevier]

Abstract: In the present paper the problems of existence and uniqueness of almost periodic solutions for impulsive cellular neural networks with delay are considered. [Copyright &y& Elsevier]

Abstract: In this paper, a novel lattice constant prediction model based on support vector machine is proposed. In this proposed technique, advanced data set generation technique is also used which is helpful to develop fairly generalized prediction models. This enables us to achieve improved prediction performance of lattice constant of structurally known perovskites. Experimental results obtained using orthorhombic ABO3 perovskites demonstrate that our proposed prediction model is more efficient, robust and fast than those based on artificial neural networks. [Copyright &y& Elsevier]

Abstract: This paper presents a model of flotation stage using a neural network to predict the efficiency and the effect of operational parameters on the efficiency of ink removing. Two methods are used to determine the kinetic parameters of the flotation process using particular experimental conditions: experimental data obtained at a laboratory level, and simulated data by means of a neural network. Simulated values obtained with a neural network correspond closely to the experimental results. Neural networks are long-range tools for studying processes when some knowledge of the phenomena that occur in the process is acquired in order to develop models based on the experimental results. The neural network model accurately reproduces all the effects of operation variables and can be used in a simulation of a deinking plant to determine the optimal operational conditions. [Copyright &y& Elsevier]

Abstract: Mechanical properties of any material are extensively influenced by the parameters such as strain, strain rate, temperature, and its composition. The characteristics of any material such as ductility, strain hardening, strength, dynamic recovery, grain growth, and recrystallization are greatly affected by the influence of various process parameters. So, it is essential to have the knowledge of the constitutive relationships that relate different process variables to flow stress of the deforming material which estimates various parameters such as load, energy, and stress in the metal forming operations. A consistent effort has been gone into developing the constitutive equations for the detailed mathematical description of the flow curves and the aforementioned parameters for years now. Soft computing tools that concern computation of an imprecise environment and model very complex systems those are based on input–output relationship have gained significant attention in recent years. The intricacies of the mathematical modeling of the mechanical properties of the material, enticed the artificial research community to take this as a challenge. One such soft computing tool neural network is applied in this field to predict the behavior accurately. In this paper, a hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels. The RSONFN is having the advantages of the well-established technologies of the artificial intelligence tools such as Fuzzy logic to capture long range data sets and neural networks. The RSONFN structure is a dynamic one as the numbers of its layers as well as the numbers of nodes in each layer of the network are not predetermined. Such an attribute differentiate it from the Multilayer perceptron which is having static structure. The results obtained by this network prove its superiority over other existing tools. [Copyright &y& Elsevier]

Abstract: The spiking neural P systems are a class of computing devices recently introduced as a bridge between spiking neural nets and membrane computing. In this paper we prove a series of normal forms for spiking neural P systems, concerning the regular expressions used in the firing rules, the delay between firing and spiking, the forgetting rules used, and the outdegree of the graph of synapses. In all cases, surprising simplifications are found, without losing the computational completeness — sometimes at the price of (slightly) increasing other parameters which describe the complexity of these systems. [Copyright &y& Elsevier]

Abstract: In this paper, the neural network based modeling for electrical characteristics of the HfO2 thin films grown by metal organic molecular beam epitaxy was investigated. The accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. The input process parameters were extracted by analyzing the process conditions and the characterization of the films. X-ray diffraction was used to analyze the characteristic variation for the different process conditions. In order to build the process model, the neural network model using the error back-propagation algorithm was carried out and those initial weights and biases are selected by Latin Hypercube Sampling method. This modeling methodology can allow us to optimize the process recipes and improve the manufacturability. [Copyright &y& Elsevier]