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This paper proposed an artificial neural network (ANN) approach based on Lagrangian multiplier method (Lagrangian ANN) to solve the problem of economic load flow in a power system. Operational requirements and transmission losses are also taken care by the proposed approach. Power plant operating costs are represented by exponential cost functions. Simulation on a test example with six generating...
This paper uses generalized congruence function instead of transfer function of classical BP neural network, and improve convergence rate of neural network. We introduce the subsection generalized derivation, error back propagation derivation mechanism of classical BP algorithm to adjust weight vector in generalized congruence neural network, and modify generalized congruence neural network, and then...
After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
Local minimum is incorporated problem in neural network (NN) training. To alleviate this problem, a modification of standard backpropagation (BP) algorithm, called BPCL for training NN is proposed. When local minimum arrives in the training, the weights of NN become idle. If the chaotic variation of learning rate (LR) is included during training, the weight update may be accelerated in the local minimum...
A forecasting model for gas emission based on wavelet neural network is proposed in this paper. In the model, wavelet neutral network (WNN) is applied to the forecasting with gradient descent and amended by validity of iteration training algorithm. Compared with back-propagation neural networks, forecasting of the model has advantages of faster convergence and more accurate. Simulation results have...
The piece of research presents a conceptual overview on diverse cognitive styles reflections in adaptable Open Learning systems. The main goal of this approach is quantitative forecasting the performance of adaptable Open Learning (equivalently e-learning) Systems using cognitive Neural Network modelling. Furthermore, analysis of interactive two diverse learners' cognitive styles with a friendly adaptable...
In this paper, the multistability is discussed for competitive neural networks (CNNs) with nondecreasing saturated activation functions with 2 r corner points. Based on decomposition of state space, Cauchy convergence principle and inequality technique, some sufficient conditions ensuring the local exponential stability of (r + 1)N equilibrium points are derived. The obtained results are less restrictive...
This paper applies the principle of the immune system adjustment to optimize the structure parameters of wavelet network, so as to establish a new type of wavelet neural network model which will be applied to turbine exhaust steam enthalpies. The calculation results show that the model has fast convergence, simple operation, high accuracy in forecasting, and has certain value of engineering applications.
In this paper, a faster supervised algorithm (BPfast) for the neural network training is proposed that maximizes the derivative of sigmoid activation function during back-propagation (BP) training. BP adjusts the weights of neural network with minimizing an error function. Due to the presence of derivative information in the weight update rule, BP goes to `premature saturation' that slows down the...
This paper addresses an interdisciplinary solution for one of the problems associated with computerized educational disciplines. Addressed problem basically concerned with realistic computer-based educational simulations (e-Sims). More specifically, it searches for optimal software learning package(s) applied for teaching of specified curriculum(s) in classroom(s). Herein, quantitative evaluation...
The essence of traditional ANN algorithm is to transfer the input-output problem of a group sample into a nonlinear programming problem. And it is a learning method to use iteration to work out weight problem along the negative gradient direction, but its convergence rate is slow and it is easy to fall into local minimum. Previously, there are many improved methods to solve the above-mentioned drawbacks...
Minor component analysis (MCA) is an important feature extraction technique which has been widely applied in data analysis fields. MCA neural networks generally are used to extract online minor component in term of adapting the demands of real time and decreasing computational complexity. However, the MCA learning algorithm can produce complicated dynamical behavior under some conditions, such as...
This paper presents a neural network method for solving a class of linear fractional optimization problems with linear equality constraints. The proposed neural network model have the following two properties. First, it is demonstrated that the set of optima to the problems coincides with the set of equilibria of the neural network models which means the proposed model is complete. Second, it is also...
Radial basis function (RBF) neural network is increasingly used to predict groundwater table, which often shows complex nonlinear characteristic. But the traditional RBF training algorithm based on gradient descent optimization method can only obtain the partial/local optimums solution sometimes. Furthermore, man-made selecting the structure of RBF neural network has blindness and expends much time...
Product quality plays an important role in facing competition and gaining competitiveness. Both Engineering Process Controllers (EPC) and Statistical Process Control (SPC) are effective methods of monitoring and adjusting the transition stages to improve process quality. At the same time, neural network was adopted to monitor the process and a flexible model is developed to determine optimal adjustable...
Due to the disadvantages of the ant algorithm used in the combining optimization in continuous space and the demerit of BP algorithm being vulnerable in the local optimum, the dynamics model of chaos ant colony has been introduced into the optimization of weights in neural network model. Therefore, the chaos ant colony neural network can have both extensive mapping ability of neural network and rapid...
This paper extends a neural network based architecture for the weighted least-squares design of IIR all-pass filters. The error difference between the desired phase response and the phase of the designed all-pass filter is formulated as a Lyapunov error criterion. The filter coefficients are obtained when neural network achieves convergence by using the corresponding dynamic function. Furthermore,...
By using QAM signals as input, this paper adopts a blind equalizer based on neural network and constant modulus algorithm. By very few training serial signals to make the network convergent, and then the equalizer changes to the blind algorithm. The simulations show that this equalizer has better performance whether at convergence speed or the remnant errors' energy, and its convergence capability...
In order to overcome the disadvantages such as low calculation precision and convergence rate of traditional BP neural network algorithm, a kind of nonlinear optimization method-BFGS method for unconstrained extreme problem is introduced into BP neural network algorithm, and a BFGS-BP neural network model is developed, which is applied well in structure deformation monitoring data processing and forecasting...
To address the unconstrained optimization problem, the Conjugate Gradient Method (CG) uses the sequence of iterations to approach the minimum point of aim function. Because of the effect of rounding errors, many merits of CG are no longer in existence in practical use. Hence the rate of convergence is not ideal and a practical problem confronting us is how to improve conjugate gradient iteration so...
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