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Abstract: This study presents a reinforcement evolutionary learning algorithm (REL) for the self-evolving neural fuzzy inference networks (SENFIN). By applying functional link neural networks (FLNN) as the consequent part of the fuzzy rules, the proposed SENFIN model combines orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. The SENFIN model can generate the consequent part of a nonlinear combination of the input variables. An efficient reinforcement evolutionary learning algorithm (REL), which consists of structure learning and parameter learning, is also presented. The structure learning is to determine the number of fuzzy rules. It adopts a subgroup symbiotic evolution to yield several variable fuzzy systems and uses an elite-based structure strategy to find the suitable number of fuzzy rules for solving a specific problem. The parameter learning is to adjust parameters of the SENFIN. It is a hybrid evolutionary algorithm, i.e., combining the cooperative particle swarm optimization and the cultural algorithm, called the cultural cooperative particle swarm optimization (CCPSO). As the result, the CCPSO approach can increase the global search capacity by using the belief space. In this paper the proposed NFIN with an efficient reinforcement evolutionary learning algorithm had been evaluated by two reinforcement learning applications, i.e., to balance the cart–pole system and the ball and beam system. Experimental results have demonstrated that the proposed approach performs well in reinforcement learning problems. [Copyright &y& Elsevier]

The article presents a study on the adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) in solving control problems. It outlines the advantages and disadvantages of the ANFN, which motivated to the development of the MODE learning algorithm. It highlights the development of the hardware-in-the loop control technique for the ANFN controller by using MODE learning algorithm (ANFN-MODE), which has been used in planetary-train-type inverted pendulum system, and the magnetic visual simulation. The study has determined the positive result of the proposed ANFN-MODE controller.

This paper introduces a recurrent neural-fuzzy network (RNFN) based on improved particle swarm optimization (IPSO) for pattern recognition applications. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional PSO. A novel MEDO combining the evolutionary direction operator (EDO) and the migration operation is also proposed. Hence, the proposed IPSO method can improve the ability of searching global solution. Experimental results have shown that the proposed IPSO method has a better performance than the traditional PSO in the human body classification and the skin color detection. [Copyright &y& Elsevier]

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems. [ABSTRACT FROM PUBLISHER]