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This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi-Sugeno-Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of fuzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability. [ABSTRACT FROM AUTHOR]