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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]

Abstract: This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for Self-Organizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure learning and parameter learning for the SONFS model. The structure learning can determine whether or not to generate a new rule-based subpopulation which satisfies the fuzzy partition of input variables using the entropy measure. The parameter learning combines two strategies including a subpopulation symbiotic evolution and a modified differential evolution. The RSMODE can automatically generate initial subpopulation and each individual in each subpopulation evolves separately using a modified differential evolution. Finally, the proposed method is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed RSMODE learning algorithm. [Copyright &y& Elsevier]

Abstract: This study presents a functional neural fuzzy network (FNFN) for classification applications. The proposed FNFN model adopts a functional neural network (FLNN) to the consequent part of the fuzzy rules. Orthogonal polynomials and linearly independent functions are used for a functional expansion of the FLNN. Thus, the consequent part of the proposed FNFN model is a nonlinear combination of input variables. The FNFN model can construct its structure and adapt its free parameters with online learning algorithms, which consist of structure learning algorithm and parameter learning algorithm. The structure learning algorithm is based on the entropy measure to determine the number of fuzzy rules. The parameter learning algorithm, based on the gradient descent method, can adjust the shapes of the membership functions and the corresponding weights of the FLNN. Finally, the FNFN model is applied to various simulations. The simulation results for the Iris, Wisconsin breast cancer, and wine classifications show that FNFN model has superior performance than other models for classification applications. [ABSTRACT FROM AUTHOR]

Abstract: Kernel-based nonlinear characteristic extraction and classification algorithms are popular new research directions in machine learning. In this paper, we propose an improved photometric stereo scheme based on improved kernel-independent component analysis method to reconstruct 3D human faces. Next, we fetch the information of 3D faces for facial face recognition. For reconstruction, we obtain the correct normal vector’s sequence to form the surface, and use a method for enforcing integrability to reconstruct 3D objects. We test our algorithm on a number of real images captured from the Yale Face Database B, and use three kinds of methods to fetch characteristic values. Those methods are called contour-based, circle-based, and feature-based methods. Then, a three-layer, feed-forward neural network trained by a back-propagation algorithm is used to realize a classifier. All the experimental results were compared to those of the existing human face reconstruction and recognition approaches tested on the same images. The experimental results demonstrate that the proposed improved kernel independent component analysis (IKICA) method is efficient in reconstruction and face recognition applications. [ABSTRACT FROM AUTHOR]

Abstract: The implementation of adaptive neural fuzzy networks (NFNs) using field programmable gate arrays (FPGA) is proposed in this study. Hardware implementation of NFNs with learning ability is very difficult. The backpropagation (BP) method in the learning algorithm is widely used in NFNs, making it difficult to implement NFNs in hardware because calculating the backpropagation error of all parameters in a system is very complex. However, we use the simultaneous perturbation method as a learning scheme for the NFN hardware implementation. In order to reduce the chip area, we utilize the traditional non-linear activation function to implement the Gaussian function. We can confirm the reasonableness of NFN performance through some examples. [Copyright &y& Elsevier]

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]

In this study, we proposed a new technique to compensate the backlight images. Two processing stages, called the backlight level detection and the backlight image compensation, are proposed. In the backlight level detection stage, we first transferred the color space to gray space by feature weighting, then obtain two backlight factors. We apply these two backlight factors to the proposed functional-link-based neuro-fuzzy network (FNFN) with immune particle swarm optimization (IPSO) for detecting compensation degree. In the backlight image compensation stage, we also proposed the adaptive cubic curve method to compensate and enhance the brightness of backlight images according to the compensation degree of each image. The backlight degree is indicated by histograms of the luminance distribution in the backlight level detection stage. The experiment results showed that the backlight images can be compensated effectively. [Copyright &y& Elsevier]

Abstract: This paper describes a self-constructing wavelet network (SCWN) controller for nonlinear systems control. The proposed SCWN controller has a four-layer structure. We adopt the orthogonal wavelet functions as its node functions. An online learning algorithm, structure learning and parameter learning, allows the dynamic determining of the number of wavelet bases, and adjusting the shape of the wavelet bases and the connection weights. The SCWN controller is a highly autonomous system. Initially, there are no hidden nodes. They are created and begin to grow as learning proceeds. Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme. [Copyright &y& Elsevier]

Abstract: This paper introduces implementation of a wavelet neural network (WNN) with learning ability on field programmable gate array (FPGA). A learning algorithm using gradient descent method is not easy to implement in an electronic circuit and has local minimum. A more suitable method is the particle swarm optimization (PSO) that is a population-based optimization algorithm. The PSO is similar to the GA, but it has no evolution operators such as crossover and mutation. In the approximation of a nonlinear activation function, we use a Taylor series and a look-up table (LUT) to achieve a more accurate approximation. The results of the two experiments demonstrate the successful hardware implementation of the wavelet neural networks with the PSO algorithm using FPGA. From the results of the experiment, it can be seen that the performance of the PSO is better than that of the simultaneous perturbation algorithm at sufficient particle sizes. [Copyright &y& Elsevier]

Abstract: A recurrent wavelet-based neuro fuzzy network (RWNFN) is proposed in this paper. The proposed RWNFN integrates wavelet transforms with fuzzy rules. Temporal relations are embedded in the network by adding feedback connections from memory units in the third layer of a feedforward wavelet-based neuro fuzzy network (WNFN). The RWNFN augments the basic ability of the WNFN to overcome temporal problems. Moreover, an online learning algorithm is proposed to automatically construct the RWNFN. Computer simulations were conducted to illustrate the performance and applicability of the proposed system. [Copyright &y& Elsevier]