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This paper presents a novel method on building relationship between the vision features of the terrain images and the terrain traversability which manifests the difficulty of field robot traveling across one terrain. Vision features of the image are extracted based on color and texture. The travesability is labeled with the relative vibration. The support vector machine regression method is adopted to build up the inner relationship between them. In order to avoid the over-learning during training, -fold method is used and average mean square error is defined as the target minimized to get the optimal parameters based on parameter space grid method. For the traveling smoothness of field robot, the original traversability prediction is transformed to computed traversability prediction based on different initial sub-regions. The optimal path is given by minimizing the sum of computed traversability prediction of all sub-regions in each path. Three experiments are discussed to demonstrate the effectiveness and efficiency of the method mentioned in this paper.

This paper presents a novel approach of designing linear phase FIR low pass and high pass filter using Random PSO in hybrid with DE known as Random PSODE (RPSODE). In this paper, the Random PSO is used which utilises the weighted particle to guide the search direction for both explorative and exploitative searches. Differential evolution (DE) is one of the very fast and robust evolutionary algorithms which has shown superior performance for continuous global optimization; uses differential information to guide its search direction but sometime causes instability problem; whereas, PSO is a robust, population based stochastic search technique but has the problem of sub-optimality. This paper efficiently combines the Random PSO and DE so as to overcome the disadvantages faced by both the algorithms individually and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show the superiority of RPSODE in global convergence properties and local search ability, and prove it to be a promising candidate for designing the FIR filters. RPSODE outperforms PSO, DE, and PSODE not only in magnitude response but in the convergence speed as well.

Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering applications for which classic control strategies do not achieve good results or for when it is too difficult to obtain a mathematical model. Recently, the International Electrotechnical Commission has published a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined common understanding of the basic means with which to integrate fuzzy control applications in control systems. In this paper, we introduce an open source Java library called jFuzzyLogic which offers a fully functional and complete implementation of a fuzzy inference system according to this standard, providing a programming interface and Eclipse plugin to easily write and test code for fuzzy control applications. A case study is given to illustrate the use of jFuzzyLogic., McGill Uninversity, Genome Quebec, Spanish Government TIN2011-28488, Andalusian Government P10-TIC-6858

The performance of Differential Evolution (DE) for multiobjective optimization problems (MOPs) can be greatly enhanced by hybridizing with other techniques. In this paper, a new hybrid DE incorporating preference based local search is proposed. In every generation, a set of nondominated solutions is generated by DE operation. Usually these solutions distribute unevenly along the obtained nondominated set. To get solutions in the sparse region of the nondominated set, a mini population and preference based local search algorithm is specifically designed, and is used to exploit the sparse region by optimizing an achievement scalarizing function (ASF) with the dynamically adjusted reference point. As a result, multiple solutions in the sparse region can be obtained. Moreover, to retain uniformly spread nondominated solutions, an improved ε-dominance strategy, which would not delete the extreme points found during the evolution, is proposed to update the external archive set. Finally, numerical results and comparisons demonstrate the efficiency of the proposed algorithm.

Visual attention mechanism is one of the important techniques in computer vision field, and it can increase the effectiveness of computer image information processing. Biogeography-Based Optimization (BBO) is a bioinspired algorithm for global optimization, searching for the global optimum mainly through two steps: migration and mutation. In this paper, a novel Predator-Prey Biogeography-Based Optimization (PPBBO) is utilized to solve the bio-inspired visual attention problem. In PPBBO method, BBO is combined with the mechanism of predatorprey, which can enhance the global convergence of the algorithm. The convergence property of the PPBBO is analyzed theoretically, and the detailed process is also given. Comparative experimental results with basic BBO, CBBO, CPPBBO, and Particle Swarm Optimization (PSO) demonstrate the feasibility and effectiveness of our presented PPBBO for adjusting combination of feature maps in visual attention.

This paper presents a hybrid wavelet-fuzzy based multiresolution (MR) controller for robust speed control of induction motor. The discrete wavelet transform (DWT) is used to decompose the error between the actual speed and command speed of the induction motor drive in to different frequency components. A self-tuning fuzzy logic is used for online tuning of the controller parameters. The proposed controller has the ability of meeting the speed tracking requirements in the closed loop system. The complete indirect field oriented control scheme incorporating the proposed wavelet-fuzzy based MR controller is investigated theoretically and simulated under various dynamic operating conditions. Simulation results are compared with that of conventional PI controller and fuzzy based PI controller. The speed control scheme incorporating the proposed controller is implemented in real time using the digital processor (DSP) control board. Simulation and experimental results validates the effectiveness of the proposed controller over conventional controllers and proves to be more suitable for high performance applications.

This paper considers the problem of non-fragile finite-time guaranteed cost fuzzy control for continuous-time nonlinear systems. Firstly, the definition on finite-time stability (FTS) for continuous-time nonlinear systems is provided and we give a novel and explicit interpretation for non-fragile finite-time guaranteed cost control. Secondly, sufficient conditions for the existence of state feedback controller are derived in terms of linear matrix inequities (LMIs), which guarantee the requirements of the provided performance criterion. The related optimization problem is also offered to minimize the guaranteed cost performance bound. Finally, an illustrative example is presented to show the validity of the proposed scheme.

As a knowledge representation tool, ontologies have been widely applied in many fields such as knowledge management and information integration, etc. Ontology measurement is an important challenge in the field of knowledge management in order to manage the development of ontology based systems and reduce the risk of project failure. This paper proposes a generic implementation framework for stable semantic ontology measurement. Through this framework, an ontology will be measured according to its semantic enriched representation model (SERM). The SERM model of an ontology can be used for stably measuring the semantics of the ontology. Then ontology metrics are integrated into the framework to measure candidate ontologies according to its SERM model. The related experiments are made to show that the framework can effectively measure the semantics of ontologies.

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield more accurate results. However, the forecasted interval of the FARIMA models may be very wide in some specific Circumstances. For instance, when data has high volatility or includes a significant difference or outliers. In this paper, a new hybrid model of FARIMA models is proposed by combining with probabilistic neural classifiers, called FARIMAH, in order to yield a more general and more accurate model than FARIMA models for financial forecasting in incomplete data situations. The main idea of the proposed model is based on this fact that the distribution of the actual values in the forecasted interval by FARIMA is not uniform. Thus, by detecting the spaces with more probability for actual values using the probabilistic classifier, narrower interval than traditional FARIMA models can be obtained. Empirical results of exchange rate markets forecasting indicate that the proposed model exhibit effectively improved forecasting accuracy, so it can be used as an alternative model to exchange rate forecasting, especially when the scant data made available over a short span of time.

Since comprehensive evaluation model was introduced by Wang in 1984, the comprehensive evaluation model has many successful applications in real life. But some examples show the inefficiency of the comprehensive evaluation model. In order to process these problems, in this paper, we introduce two concepts of standardized fuzzy set and normalized fuzzy set, propose a novel method to improve the comprehensive evaluation model and its algorithm. Finally, we use three numerical examples to illustrate our proposed model reasonable and point out that our proposed method can avoid some inefficiencies in applications.

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent "conceptual worlds" that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering "approximation" methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds. © 2013 Copyright the authors.

Every day there are more techniques that can work with low quality data. As a result, issues related to data quality have become more crucial and have consumed a majority of the time and budget of data mining projects. One problem for researchers is the lack of low quality data in order to test their techniques with this data type. Also, as far as we know, there is no software tool focused on the create/manage low quality datasets which treats, in the widest possible way, the low quality data and helps us to create repositories with low quality datasets for testing and comparison of data mining techniques and algorithms. For this reason, we present in this paper a software tool which can create/manage low quality datasets. Among other things, the tool can transform a dataset by adding low quality data, removing and replacing any data, constructing a fuzzy partition of the attributes, etc. It also allows different input/output formats of the dataset.

Content-based video retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based video retrieval, feature combination plays a key role. As a result content-based retrieval of all different type video data turns out to be a challenging and vigorous problem. This paper presents an effective content based video retrieval system, which recognizes and retrieves videos with three different types of visual effects. The raw video information is divided into shots and also the object feature, movement feature and also the occlusion options are extracted from these shots and also the feature library is used for the storage method of those options. Advanced on, the Kullback-Leibler distance is computed among the options of the feature library and also the options of the question clip that's extracted within the similar manner. The results show that it is possible to improve a system for content-based video retrieval by using Kullback-Leibler distance model, which takes careful consideration of the structure and distribution of visual features. Hence the final results with the aid of the Kullback-Leibler distance the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.