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The assimilation of formal logic into the domain of Software Engineering offers the possibility of enormous benefits in terms of software reliability and verifiability. To date, however, the integration of such techniques has proved difficult since they involve a significantly increased burden on the programmer in meeting the demands of the formal mechanisms being employed. The current paper investigates the advantages which may be gained by the software development process with the introduction of Artificial Neural Network technology into a formal software development system. Essentially, the adaptive artificial neural network model is employed to refine an existing formal software model in order to produce increasingly better approximations to a given solution. Each approximation is itself a valid formal system whose precise behaviour may be formally determined. The paper introduces a framework by which a programmer may define a system possessing the abstract structure of a traditional neural network but whose internal structures are taken from the formal mathematical domain of Constructive Type Theory. The system will then refine itself to produce successive approximations to a desired goal based on data presented to it. An example is presented addressing a problem domain which has previously proved difficult to model. Although the example presented is necessarily limited, it does provide an insight into the potential advantages of merging formal logic with artificial neural systems. [ABSTRACT FROM AUTHOR]

In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

Although the current E-Learning systems have many merits, many of them only treat advanced information technology as simple communication tools, and release some learning contents and exercises in the network. In this paper, a one-class-in-one network for emotion recognition system in E-learning is proposed and implemented in the paper. Using a large database of phoneme-balanced Chinese words read by speakers consciously trying to portray an emotion, we trained and tested this module. We achieved a recognition rate of approximately 55%. The results obtained in this study demonstrate that emotion recognition in speech is feasible, and that neural networks are well suited for this task. [ABSTRACT FROM AUTHOR]

Recent years have witnessed intense research in the general area of Multi-Classifier systems (MCS), but this has rarely incorporated any utilisation of weightless neural systems(WNS) as the combiner of an MCS ensemble. This paper explores the application of weightless networks within the multi-classifier environment by introducing an intelligent multiclassifier system using a WNS called the Enhanced Probabilistic Convergent Neural Networks (EPCN). The paper explores the use of EPCN by illustrating its major features, such as the specification of disjoint or overlapping input subset to the MCS, and the inherently parallel nature of the design. Within the proposed system, the number of base classifiers per MCS could be specified manually or automatically. . The proposed MCS is problem-domain independent and, our investigation is performed on handwritten characters. The proposed MCS is adaptive, its combiner is capable of extracting absolute or weighted classification decision(output) from base classifier. Diversity is increased in the base classifier by injecting randomness into the system parameters. Two types of EPCN classifiers are proposed, fix-PCN and rand-PCN. These PCNs are independent and orthogonal. One uses a fixed method of forming connectivity while the other uses random method of forming connectivity. In order to verify the performance of the recognition system, tests were performed, off-line, on benchmark datasets of unconstrained handwritten numerals. Experimental results suggest that MCS outperforms single EPCN in classification of handwritten characters. [ABSTRACT FROM AUTHOR]

Vibration detection and control strategies strive to reduce the effect of harmful vibrations on machinery and people. In general, vibration control strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there are more limits to their effectiveness. Active vibration control strategies, on the other hand, can be very effective but require more complex algorithms and are especially susceptible to time delays. This paper introduces an active and adaptive vibration control system which, based on using neural network and newest digital signal processing techniques, automatically detects noisy sinusoidal vibration parameters of a cantilever beam and generates control signals to an actuator to cancel the vibration. The control signal is generated based on calculations of a neural network and real-time digital processing. The system can repeat the vibration detection and control loop in every 25 ms, and the newest control signal is added to the original one to minimize the beam vibration. The system has been evaluated experimentally and the results showed its validity. [ABSTRACT FROM AUTHOR]

One of the most important issues in the world of financial management is investment assurance for legal and rightful persons. The existence of suitable standards and instruments to evaluate financial conditions and companies situations can help investors to under take their decisions making. Financial corruption and bankruptcy bear extensive costs which cause lasting damages to country's economy. One of the ways to prevent financial corruption is to forecast financial distress. In this paper which uses the correlation research methodology employing Artificial Neural Networks (ANN), the financial corruption of the companies has been forecasted. The main aim of this research is to compare ANN and Multi Decimal Analysis (MDA) models in bankruptcy forecasting field. In order to study the effect of sampling differences on forecasting, the absolute validation method has been used. Dual Comparison Test was used to test three hypothesises introduced in this research. The results obtained from the models based on the data from 80 accepted companies in Tehran Stock Exchange show that in forecasting financial distress, the ANN model is more accurate than the MDA model. [ABSTRACT FROM AUTHOR]

In this paper, we provide a geometric construction of a transversal homoclinic orbit for a nonlinear model neuron with time delays. The existence of chaos for time-delay systems in a high-dimensional space is theoretically confirmed with several conditions. These units may be used as basic elements for networks with higher-order information processing capabilities. [ABSTRACT FROM AUTHOR]

Software reliability is one of the most important software quality attribute and Software reliability estimation is a hard problem to solve accurately. However for management of software quality and standard practice of the organization, accurate reliability estimation is important. Non-homogeneous Poisson Process (NHPP) models and Artificial Neural Network (ANN) models are among the most important software reliability growth models. In this paper we study an approach using past fault-related data with cubic spline Network model to estimate reliability. A numerical example is shown with simulated datasets. The example shows that the proposed model accurately estimate the software reliability. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

Face recognition is a biometric identification method which among the other methods such as, finger print identification, speech recognition, signature and hand written recognition has assigned a special place to itself. In principle, the biometric identification methods include a wide range of sciences such as machine vision, image processing, pattern recognition neural networks and has various applications in film processing, control access networks and etc. There are several methods for recognition and appearance based methods is one of them. One of the most important algorithms in appearance based methods is linear discriminant analysis (LDA) method. One of the drawbacks for LDA in face recognition is the small sample size (SSS) problem so it is suggested to first reduce the dimension of the space using methods among which, principal component analysis (PCA) is the most popular one. In this paper we show that there exist stronger methods such as discrete cosine transform (DCT). [ABSTRACT FROM AUTHOR]

This paper presents a neural network global PID-sliding mode control method for the tracking control of robot manipulators with bounded uncertainties. A certain sliding mode controller with PID sliding function is developed. In this controller, the switching gain is tuned by a RBF neural network on the reachable condition of sliding mode. Thus, the effect of chattering can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding function. Mathematical proof of the stability and convergence of the control system is given. Simulation results demonstrate that the chattering and the steady state errors are eliminated and satisfactory trajectory tracking is achieved. [ABSTRACT FROM AUTHOR]

This paper is concerned with the application of a conversational agent and expert system to provide a natural language interface to a database. Typically, natural language database interfaces (NLDI's) use grammatical and/or statistical parsing. Conversational agents take a different approach, capturing key elements of user input which then trigger pre-determined output templates. It is assumed that the type of natural language questions which could be asked of a specific relational database will contain a limited number of key words (attributes), which could be captured by a conversational agent. In the proposed system, once a conversational agent has identified all relevant attributes and their values, an expert system would then apply rule based reasoning on these attributes to construct an SQL query. The knowledge base of the expert system would contain information on the database structure (metadata) and on the different possible structures of SQL queries. This would result in a real time system, which could extract both database attributes and attribute values from the user input and automatically apply a rule based reasoning system to determine the answer the user's query. [ABSTRACT FROM AUTHOR]

Seam pucker grade is one of the most important quality parameters in garments manufacturing industry. At present, seam pucker is usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. Instead of subjective evaluation, this paper presents an objective method by using image analysis and pattern recognition. The evaluation system consists of image acquisition, image normalization, feature extraction and self organizing map classifier. Textural features of seam puckers are studied with a widely used statistical method, the co-occurrence matrix approach. The grades of seam puckers can be obtained from the trained self organizing map classifier and the results are very promising. [ABSTRACT FROM AUTHOR]

The design of intelligent controllers for nonlinear systems continues to be a challenging problem, particularly when the system is uncertain or the environment noisy. A nonparametric approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (GANFIS control). Inherent in the design of such controllers is the need to tune the weights of the GANFIS controller. Evolutionary learning has been suggested to tune the GANFIS parameters but a difficulty is selecting the parameters for tuning. Further, it is well known that proper selection of the fitness function has an important effect on system performance. In this paper, we integrate two design techniques that we have previously developed into a single generalized ANFIS controller: adaptive tuners to select critical evolutionary parameters and a predictive fitness function for measuring system performance. The adaptive tuners also employ this predictive fitness as part of selection process which is a new approach. Results show that this approach is a feasible method in designing GANFIS controllers using evolutionary tuning and predictive fitness. [ABSTRACT FROM AUTHOR]

This paper presents an energy back-propagation algorithm (EBP). Learning and convergence processes of the standard backpropagation algorithm (SBP) are based on the energy function. The energy function is used with the convergence process to extract the nearest image for the unknown tested image. The EBP algorithm shows considerably better performance in terms of time of learning, time of convergence, and size of input image compared to the SBP algorithm. [ABSTRACT FROM AUTHOR]

Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce fuzzy swarm particle optimization technique for convergence of associative neural memories based on fuzzy set theory. An FPSO consists of clustering of swarm's particle by applying fuzzy c-mean algorithm to attain the neighborhood best. We present a singular value decomposition method for the selection of efficient rule from a given rule base required to attain the global best. Finally, we illustrate the proposed method by virtue of some examples. [ABSTRACT FROM AUTHOR]

Container throughput plays a major role in the development of operational strategies and long-term investment plans for container terminals. Support Vector Machine (SVM), as a powerful technique for regression and classification, shows impressive performance in the area of time series analysis. This paper presents a modified version of SVM, called the least squares support vector machine (LS-SVM), as an effective technique to forecast the monthly container throughput in Hong Kong. It also proposes a method for fast training LS-SVM by employing an approximate approach to accelerate the training process and lower the memory requirement. The proposed Approximate LS-SVM (ALSSVM) has a shorter training time than LS-SVM and a forecasting accuracy comparable to that of the standard SVM and the LS-SVM. To evaluate the effectiveness of ALSSVM, numerical experiments are conducted to compare its performance with that of standard SVM, LS-SVM and RBF Neural Network for forecasting Hong Kong's container throughput. The results show that the proposed method is an excellent forecasting tool for logistics management. [ABSTRACT FROM AUTHOR]