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Accepted Papers

  • Arabic Digit Recognition By Adaptive Network Based Fuzzy Inference System
    Samiya Silarbi, Bendahmane Abderrahmane; And Abdelkader Benyettou , University of Sciences and Technology Oran USTO-MB, Algeria
    ABSTRACT
    This paper presents the application of Adaptive Network Based Fuzzy Inference System ANFIS on spoken Arabic digit recognition. The primary tasks of fuzzy modeling are structure identification and parameter optimization: the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. Thus, determination of an appropriate structure becomes an important issue where subtractive clustering is applied to define an optimal initial structure and obtain small number of rules. The appropriate learning algorithm is performed on spoken Arabic digit dataset supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. Finally, hybrid learning combines the gradient decent and least square estimation LSE of parameters network. The results obtained show the effectiveness of themethod in terms of recognition rate and number of fuzzy rules generated.


  • Delay-Dependent Stability Of Hopfield Neural Networks With Multiple Delays
    Grienggrai and Rajchakit, Maejo University, Thailand.
    ABSTRACT
    Back propagation (BP) neural network is used to approximate the dynamic character of nonlinear discrete-time system. Considering the unmodeling dynamics of the system, the weights of neural network are updated by using a dead-zone algorithm and a robust adaptive controller based on the BP neural network is proposed. For the situation that jumping change parameters exist, multiple neural networks with multiple weights are built to cover the uncertainty of parameters, and multiple controllers based on these models are set up. At every sample time, a performance index function based on the identification error will be used to choose the optimal model and the corresponding controller. Different kinds of combinations of fixed model and adaptive model will be used for robust multiple models adaptive control (MMAC). The proof of stability and convergence of MMAC are given, and the significant efficacy of the proposed methods is tested by simulation. This paper deals with the problem of delay-dependent stability criterion of delay-difference system with multiple delays of Hopfield neural networks. Based on quadratic Lyapunov functional approach and free-weighting matrix approach, some linear matrix inequality criteria are found to guarantee delay-dependent asymptotical stability of these systems. And one example illustrates the exactness of the proposed criteria.


  • Synchronization of Homoclinic Trajectories in Lu Systems
    Manlika and Rajchakit, Maejo University, Thailand.
    ABSTRACT
    The general method for proving the existence of homoclinic trajectories in dissipative systems is developed. The applications of this method to Lorenz-like systems: Lorenz, Shimizu–Morioka, Lu and Chen systems are demonstrated. A criterion for the existence of a homoclinic trajectory within a given family of differential equations (Fishing principle) is presented. New numerical algorithm for the approximation of a homoclinic point in parameters space is constructed. The comparison with Kaplan–Yorke and Shilnikov results is made. In this paper, we study Lu’s system. First, we control the chaotic behavior of Lu’s system to its equilibrium points using linear feedback control and adaptive control method. Finally, we study chaos synchronization of Lu’s system by using active control methods.


  • Ant Colony Optimization For Capacity Problems
    Tad Gonsalves and Takafumi Shiozaki, Sophia University, Japan.
    ABSTRACT
    This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the number of trains that depart from the station in unit interval of time. The railway capacity optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP), with the TSP nodes representing the train arrival /departure events and the TSP total cost representing the total time-interval of the schedule. The application problem is then optimized using the ACO algorithm. The simulation experiments validate the formulation of the railway capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those produced by the domain experts.


  • Feature Selection: A Novel Approach For The Prediction Of Learning Disabilities In School Aged Children
    Sabu M.K, M.E.S College, India
    ABSTRACT
    Feature selection is a problem closely related to dimensionality reduction. A commonly used approach in feature selection is ranking the individual features according to some criteria and then search for an optimal feature subset based on an evaluation criterion to test the optimality. The objective of this work is to predict more accurately the presence of Learning Disability (LD) in school aged children with reduced number of symptoms. For this purpose, a novel hybrid feature selection approach is proposed by integrating a popular Rough Set based feature ranking process with a modified backward feature elimination algorithm. The approach follows a ranking of the symptoms of LD according to their importance in the data domain. Each symptoms significance/priority values reflect its relative importance to predict LD among the various cases. Then by eliminating least significant features one by one and evaluating the feature subset at each stage of the process an optimal feature subset is generated. The experimental results shows the success of the proposed method in removing redundant attributes efficiently from the LD dataset without sacrificing the classification performance.


  • Multiclass Recognition With Multiple Feature Trees
    Guan-Lin Li, Jia-Shu Wang, Chen-Ru Liao, Chun-Yi Tsai, and Horng-Chang Yang ,National Taitung University, Taiwan.
    ABSTRACT
    This paper proposes a multiclass recognition scheme which uses multiple feature trees with an extended scoring method evolved from TF-IDF. Feature trees consisting of different feature descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The experimental results show that the proposed scoring method combing with the proposed multiple feature trees yields high accuracy for multiclass recognition and achieves significant improvement compared to methods using a single feature tree with original TF-IDF.