Maryam Ashtari Mahini; Mohammad Teshnehlab; Mojtaba Ahmadieh khanehsar
Volume 1, Issue 3 , August 2015, , Pages 1-8
Abstract
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two ...
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Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsystem identification by Hammerstein-Wiener neural networkis finding model order, state matrices and system matrices. Wepropose a robust approach for identifying the nonlinear systemby neural network and subspace algorithms. The subspacealgorithms are mathematically well-established and noniterativeidentification process. The use of subspace algorithmmakes it possible to directly obtain the state space model.Moreover the order of state space model is achieved usingsubspace algorithm. Consequently, by applying the proposedalgorithm, the mean squared error decreases to 0.01 which isless than the results obtained using most approaches in theliterature.
Pattern Analysis and Intelligent Systems
Vahid Seydi Ghomsheh; Mohamad Teshnehlab; Mehdi Aliyari Shoordeli
Volume 1, Issue 2 , May 2015, , Pages 29-38
Abstract
This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function ...
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This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problems
Nazal Modhej; Mohammad Teshnehlab; Mashallah Abbasi Dezfouli
Volume 1, Issue 1 , February 2015, , Pages 37-42
Abstract
Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy ...
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Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon or slower convergence speed due to larger fixed or smaller fixed learning rate respectively. The present research deals with offering two solutions for this problem. The original idea of the present research is using changeable learning rate at each state of training phase in the CMAC model. The first algorithm deals with a new learning rate based on reviation of learning rate. The second algorithm deals with number of training iteration and performance learning, with respect to this fact that error is compatible with inverse training time. Simulation results show that this algorithms have faster convergence and better performance in comparison to conventional CMAC model in all training cycles.