Adaptive synchronization between two non-identical BAM neural networks with unknown parameters and time-varying delays

International Journal of Control, Automation and Systems - In this paper, the synchronization of two non-identical bidirectional associative memory (BAM) neural networks with unknown parameters and...     

Optimal control problem via neural networks

This paper attempts to propose a new method based on capabilities of artificial neural networks, in function approximation, to attain the solution of optimal control problems. To do so, we try to approximate the solution of Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. In the proposed error function, we used trial solutions for the trajectory function, control function and the Lagrange multipliers. These trial solutions are constructed by using neurons. Then, we minimize the error function that contains just the weights of the trial solutions. Substituting the optimal values of the weights in the trial solutions, we obtain the optimal trajectory function, optimal control function and the optimal Lagrange multipliers.     

An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem

Neural Processing Letters - In the present research, we are going to obtain the solution of the fuzzy shortest path (FSP) problem. According to our search in the scientific reported papers, this is...     

Artificial neural network approach for solving fuzzy differential equations

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples.     

An application of a merit function for solving convex programming problems

This paper presents a gradient neural network model for solving convex nonlinear programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent unconstrained minimization problem with objective energy function. A gradient model is then defined directly using the derivatives of the energy function. It is also shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. It is also found that a larger scaling factor leads to a better convergence rate of the trajectory. The validity and transient behavior of the neural network are demonstrated by using various examples.     

Fuzzy cost support vector regression on the fuzzy samples

This paper presents a new version of support vector regression (SVR) named Fuzzy Cost SVR (FCSVR) with a unique property of operating on fuzzy data where fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown to be superior in the experimental results as a property compared with conventional SVR.     

Solution of linear optimal control systems by differential transform method

In this paper, we obtain the approximate solutions for optimal control of linear systems, which have a quadratic performance index. The differential transform method (DTM) is applied for solving the extreme conditions obtained from the Pontryagin’s maximum principle. The differential transform method is one of the approximate methods, which can be easily applied to many linear and nonlinear problems and is capable of reducing the size of computational work. Applying DTM, we construct an optimal feedback control law. The results reveal that the proposed method are very effective and simple. Comparisons are made between the results of the proposed methods, homotopy perturbation method, Adomian decomposition method and exact solutions.     

An Artificial Neural Network for Solving Distributed Optimal Control of the Poisson’s Equation

Neural Processing Letters - This paper presents a simple and efficient method based on artificial neural network to solve distributed optimal control of Poisson’s equation with Dirichlet...     

A new fuzzy neural network model for solving fuzzy linear programming problems and its applications

This paper is concentrated on two types of fuzzy linear programming problems. First type with fuzzy coefficients in the objective function and the second type with fuzzy right-hand side values and fuzzy variables. Considering fuzzy derivative and fuzzy differential equations, these kinds of problems are solved using a fuzzy neural network model. To show the applicability of the method, it is applied to solve the fuzzy shortest path problem and the fuzzy maximum flow problem. Numerical results illustrate the method accuracy and it’s simple implementation.