Identification of MIMO Systems Using Neuro-Fuzzy Models with a Shuffled Frog Leaping Algorithm

In this paper, a TSK-type Neuro-fuzzy Inference System that combines the features of fuzzy sets and neural networks has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA).

Authors:

• Sana Bouzaida
• Anis Sakly
• Faouzi M'Sahli

Keywords:

• Identification
• Shuffled frog Leaping Algorithm (SFLA)
• TSK-type neuro-fuzzy model.

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