Catálogo de publicaciones - libros

The paper concerns the simultaneous optimization for structure and parameters of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. HFCGA is used to optimize structure and parameters of ANFIS-based fuzzy model simultaneously. The granulation is realized with the aid of the C-means clustering. Through the simultaneous optimization mechanism to be explored, we can find the overall optimal values related to structure as well as parameter identification of ANFIS-based fuzzy model via HFCGA, C-Means clustering and standard least square method. A comparative analysis demon-strates that the proposed algorithm is superior to the conventional methods.

In this paper, we present a neural network based optimization method for solving a kind of quadratic programming problems (QPPs) with equality and inequality constraints. The proposed method is appropriate for distributed implementation and can be used as a basic optimization module for managing optimization problems of large distributed systems. We test the proposed method in a real PC-Network for power system state estimation problem. Several cases are considered and obtain some successfully results.

Multilayer perceptron networks have been successfully trai- ned by error backpropagation algorithm. We show that Particle Swarm Optimization(PSO) with minimum velocity constraints can efficiently be applied to train multilayer perceptrons to overcome premature convergence and alleviates the influence of dimensionality increasing. The experiments of two multilayer perceptrons trained by PSO with minimum velocity constraints are carried out. The result clearly demonstrate the improvement of the proposed algorithm over the standard PSO in terms of convergence.

In this paper, p-n junction formation using screen-printed metallization and co-firing is used to fabricate high-efficiency solar cells on single-crystalline (SC) silicon substrates. In order to form high-quality contacts, co-firing of a screen-printed Ag grid on the front and Al on the back surface field is implemented. These contacts require low contact resistance, high conductivity, and good adhesion to achieve high efficiency. Before co-firing, a statistically designed experiment is conducted. After the experiment, a neural network (NN) trained by the error back-propagation algorithm is employed to model the crucial relationships between several input factors and solar cell efficiency. The trained NN model is also used to optimize the beltline furnace process through genetic algorithms.

Collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) approaches, is a two-level optimization method for large-scale and distributed-analysis engineering design problem. In practical application, CO exists some known weaknesses, such as slow convergence, complex numerical computation, which result in further difficulties when modeling the satisfaction degree in CO. This paper proposes the use of approximation response model in place of discipline-level optimization in order to relieve the aforementioned difficulties. In addition, a satisficing back propagation neural network based on multiple-quality and multiple-satisfaction mapping criterion is applied to the design of the satisfaction degree approximation for disciplinary objective. An example of electronic packaging problem is provided to demonstrate the feasibility of the proposed method.

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension of particle swarm containing particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis.

With the development of BP-ISP alignment research, how to evaluate the strategy alignment of BP-ISP become the key problem of this field. In this paper, a set of index system of evaluating the alignment of BP-ISP is established. Based on the index system, an integration algorithm with PSO and neural network is established to evaluate the alignment of BP-ISP. In order to verify the effectiveness of the method, a real case is given and BP neural network is also used to assess the same data. The experimental results show that integration algorithm with PSO and neural network is effective in the alignment evaluation of BP-ISP and achieves better performance than BP neural network.

In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

This paper presents an efficient approach based on Hopfield network for solving nonlinear optimization problems, with polynomial objective function, polynomial equality constraints and polynomial inequality constraints. A modified Hopfield network is developed and its stability and convergence is analyzed in the paper. Then a mapping of nonlinear optimization problems is formulated using the modified Hopfield network. Simulation results are provided to demonstrate the performance of the proposed neural network.

An artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm has been introduced to compensate the polarization mode dispersion (PMD) in the ultra-high speed optical communication system. The hybrid algorithm, also referred to as PSO-BP algorithm, has been adopted to train the weights of ANN, and it can make use of not only strong global searching ability of the PSO algorithm, but also strong local searching ability of the BP algorithm. In the proposed algorithm, a heuristic way was adopted to give a transition from particle swarm search to gradient descending search. The experimental results show that the hybrid algorithm is better than the Adaptive PSO algorithm and BP algorithm in convergent speed and convergent accuracy. And in the PMD compensation system, the ANN is used to optimize the degree of polarization (DOP) signal, which can achieve the random stochastic PMD compensation adaptively. Simulation results show the opening of eye diagram can be improved obviously.