Structure Optimization of Locally Linear Model Tree Using Extermal Optimization

Subject Areas : Renewable energy

Khalil Sharifi ¹ , Mohammad Reza Ahmadzadeh ²

1 - MSc/Islamic Azad University, Najafabad Beranch
2 - Assistant Professor/ Isfahan University of Technology

Received: 2009-10-01 Accepted : 2011-04-29 Published : 2011-05-01

Keywords: Local linear neuro - fuzzy models, nonlinear system identification, locally linear model tree (LOLIMOT), extremal optimizatopn (EO),

Abstract :

Locally Linear Model Tree (LOLIMOT) algorithm proposed by Nelles deals with local linear nearo-fuzzy models that is based on divides-and-conquer strategy that a complex modeling problem is divided to a number of smaller and thus simpler sub problems. So the characteristic of such a neuro-fuzzy model depends on division strategy for the original complex problem. For finding the best output the algorithm divides the problem to a number of local linear models (LLMs) , then continues with finding the worst LLM and dividing it. LOLIMOT splits the local linear models into two equal halves with an axis-orthogonal decomposition strategy. In this paper a new approach based on extremeal optimization (EO) is used to optimize the structure of LOLIMOT. Simulation results show the effectiveness of the enhanced LOLIMOT to have a higher precision with optimal number of neurons.