The proposed neural network is an extension of Newton deepest decedent method for constraint problems, it can improve the accuracy of the solutions, and its structure is simpler than the existing networks even when it is for solving positive definite quadratic programming problems.TAO Qing...
Optimality and Duality for an Efficient Solution of Multiobjective Nonlinear Fractional Programming Problem Involving Semilocally Convex Functions Stability of Discrete time Recurrent Neural Networks and Nonlinear optimization problems Optimality Conditions and Duality of Three Kinds of Nonlinear Fractional Programm...
Improved neural networks for linear and nonlinear programming International Journal of Neural Systems (1992) L.O Chua et al. Nonlinear programming without computation IEEE Transactions on Circuits and Systems (1984) Cichocki, A., and Bargiela, A., “Neural networks for solving linear inequality syste...
AimTo construct the simple and feasible neural networks for solving a class of linearly constrained convex programming problems.MethodsThe projection method and Lyapunov′s direct method.ResultsTwo primal neural-network models for solving a class of linearly constrained convex programming problems are propo...
Feasibility and efficiency of the proposed neural networks are supported by simulation experiments. Moreover, the feedback neural network can also be applied to solve general nonlinear convex programming and nonlinear monotone variational inequalities problems with convex constraints. 展开 关键词: ...
We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave...
Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks1,2. Non-enzymatic networks could in
A New Nonlinear Neural Network for Solving QP Problems In this paper, a new nonlinear neural network is proposed to solving quadratic programming problems subject to linear equality and inequality constraints w... Y Yan - International Symposium on Neural Networks 被引量: 3发表: 2014年 Neural Net...
来源会议 International Joint Conference on Neural Networks 2011 研究点推荐 Neural-network-based optimal control control constraints nonlinear cdiscrete-time systems neural-network-based optimal control scheme nonlinear discrete-time systems rative adaptive dynamic programming 引用走势 2012 被引量:3 站内...
Neural networks (also called neural nets) are neural-inspired nonlinear models for supervised learning. As we will see, neural nets can be viewed as natural, more powerful extensions of supervised learning methods such as linear and logistic regression and soft-max methods. 9.1.1 The basic buildi...