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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]