Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms

Maryam Ashtari Mahini; Mohammad Teshnehlab; Mojtaba Ahmadieh khanehsar

Volume 1, Issue 3 , August 2015, , Pages 1-8

  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 ...  Read More

Pattern Analysis and Intelligent Systems
Adaptive Rule-Base Influence Function Mechanism for Cultural Algorithm

Vahid Seydi Ghomsheh; Mohamad Teshnehlab; Mehdi Aliyari Shoordeli

Volume 1, Issue 2 , May 2015, , Pages 29-38

  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 ...  Read More

Two Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate

Nazal Modhej; Mohammad Teshnehlab; Mashallah Abbasi Dezfouli

Volume 1, Issue 1 , February 2015, , Pages 37-42

  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 ...  Read More