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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]

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 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]