scholarly journals A Recurrent Neural Network for Nonlinear Fractional Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Quan-Ju Zhang ◽  
Xiao Qing Lu
Keyword(s):  
Neural Network ◽  
Fractional Programming ◽  
Initial Point ◽  
Optimal Solution ◽  
Feasible Region ◽  
Globally Convergent ◽  
The Neural Network ◽  
Interval Constraints ◽  
Simulation Results ◽  
Nonlinear Fractional Programming

This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

Letter to the Editor: A DISCRETE-TIME LAGRANGIAN NETWORK FOR SOLVING CONSTRAINED QUADRATIC PROGRAMS

2000 ◽  
Vol 10 (04) ◽  
pp. 261-265 ◽  
Author(s):  
WAI SUM TANG ◽  
JUN WANG
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Optimization Method ◽  
Quadratic Program ◽  
Penalty Parameter ◽  
Quadratic Programs ◽  
The Neural Network ◽  
Simulation Results

A discrete-time recurrent neural network which is called the discrete-time Lagrangian network is proposed in this letter for solving convex quadratic programs. It is developed based on the classical Lagrange optimization method and solves quadratic programs without using any penalty parameter. The condition for the neural network to globally converge to the optimal solution of the quadratic program is given. Simulation results are presented to illustrate its performance.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

2011 ◽  
Vol 55-57 ◽  
pp. 407-412 ◽  
Author(s):  
Ye Yuan ◽  
Zhong Kai Yang ◽  
Qing Fu Li
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Input Signal ◽  
Inductive Reasoning ◽  
Qualitative Modeling ◽  
End Effect ◽  
Predictive Capability ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Simulation Results

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

scholarly journals A Compact Gradient Based Neural Network for Capon Spectral Estimation

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Spectral Estimation ◽  
Original Method ◽  
Vector Product ◽  
Neural Network Architecture ◽  
Computational Burden ◽  
The Neural Network ◽  
Gradient Based ◽  
Simulation Results

This paper describes the use of a novel gradient based recurrent neural network to perform Capon spectral estimation. Nowadays, in the fastest algorithm proposed by Marple et al., the computational burden still remains significant in the calculation of the autoregressive (AR) Parameters. In this paper we propose to use a gradient based neural network to compute the AR parameters by solving the Yule-Walker equations. Furthermore, to reduce the complexity of the neural network architecture, the weights matrixinputs vector product is performed efficiently using the fast Fourier transform. Simulation results show that proposed neural network and its simplified architecture lead to the same results as the original method which prove the correctness of the proposed scheme.

A Neural Network Based Adaptive Tracking Controller for Nonholonomic Wheeled Mobile Robots With Unknown Dynamics

2010 ◽  
Author(s):  
Omid Mohareri ◽  
Rached Dhaouadi
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Wheeled Mobile Robots ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
Robot Tracking ◽  
Control Scheme ◽  
Tracking Motion ◽  
Backstepping Controller ◽  
Simulation Results

This paper presents the design, implementation and comparative analysis of an intelligent neural network based controller used for adaptive trajectory tracking of a wheeled mobile robot with unknown dynamics. In this proposed control scheme, the neural network is used to continuously tune the gains of the kinematic based controller in a backstepping structure. The online learning and adaptive capabilities of the neural network are utilized to achieve a smooth and fast robot tracking motion. The simulation results are used to verify the tracking performance of the proposed control algorithm and to compare it with the conventional backstepping controller.

A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming

2008 ◽  
Vol 20 (5) ◽  
pp. 1366-1383 ◽  
Author(s):  
Qingshan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Linear Programming ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Activation Function ◽  
High Gain ◽  
Discontinuous Activation ◽  
The Neural Network ◽  
Decision Variables ◽  
Discontinuous Activation Function

A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

Neural Network Analysis of the Magnetic Bearing Systems

2010 ◽  
Vol 29-32 ◽  
pp. 190-196
Author(s):  
Hong Ya Fu ◽  
Ping Fan Liu ◽  
Qing Chun Zhang ◽  
Guo Dong Li
Keyword(s):  
Neural Network ◽  
Complete System ◽  
Magnetic Bearing ◽  
Neural Network Analysis ◽  
Robust Performance ◽  
Learning Capability ◽  
The Neural Network ◽  
Simulation Results ◽  
Pid Neural Network ◽  
Neural Network Controllers

In order to overcome the system nonlinear instability and uncertainty inherent in magnetic bearing systems, two PID neural network controllers (BP-based and GA-based) are designed and trained to emulate the operation of a complete system. Through the theoretical deduction and simulation results, the principles for the parameters choice of two neural network controllers are given. The feasibility of using the neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The robust performance and reinforcement learning capability in controlling magnetic bearing systems are compared between two PID neural network controllers.

GA-BP Neural Network Based Tire Noise Prediction

2012 ◽  
Vol 443-444 ◽  
pp. 65-70 ◽  
Author(s):  
Yong Che ◽  
Wang Xin Xiao ◽  
Li Jun Chen ◽  
Zhi Chu Huang
Keyword(s):  
Neural Network ◽  
Optimal Solution ◽  
Noise Prediction ◽  
Bp Network ◽  
Tire Noise ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
Neural Network Structure ◽  
Trained Neural Network ◽  
Set Up

According to the complexity and the highly nonlinear characteristics of the tire sound, various parameters affecting tire noise were analyzed. By employing neural network a new method of tire noise prediction was proposed. Combining BP neural networks with genetic algorithms the noise prediction model was set up. In order to effectively predict tire noise, the neural network structure was designed and the input and output parameters of the network were determined. The genetic algorithm was added to the BP network in order to optimize initial weights and search out the optimal solution of the network. Applying laboratory drum method large amounts of tire noise test samples were obtained to train the BP network. Trained neural network can accurately predict tire noise in range of typical frequency bands. The results show that precision of this method is sufficient and the prediction effect is better.

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