In this paper, a neural network-driven fuzzy reasoning system for stock price forecast is proposed on the basis of improved Takagi-Sugeno reasoning model. The experimental result shows that the fuzzy neural network has such properties as fast convergence, high precision and strong function approximation ability and is suitable for real stock price prediction.

Abstr,,ct. In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set ofaffine functionals can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of sing...

We consider the approximation of smooth multivariate functions in C(I R d) by feedforward neural networks with a single hidden layer of non-linear ridge functions. Under certain assumptions on the smoothness of the functions being approximated and on the activation functions in the neural network, we present upper bounds on the degree of approximation achieved over the domain IR d , thereby gen...

Abstr,,ct. In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set ofaffine functionals can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of sing...

We consider the approximation of smooth multivariate functions in C(IRd) by feedforward neural networks with a single hidden layer of nonlinear ridge functions. Under certain assumptions on the smoothness of the functions being approximated and on the activation functions in the neural network, we present upper bounds on the degree of approximation achieved over the domain IRd, thereby generali...

• Proposing a self-adaptive algorithm (ANE) for designing nearly optimal two-layer NN solving PDEs. A method of continuation by the ANE providing good initialization in training neural network. Analyzing effect numerical integration. Introducing posteriori error estimators recovery type method. Demonstrating superior performance problems with interface singularities and sharp interior layers. I...

This paper discusses an outer-approximation guided optimization method for constrained neural network inverse problems with rectified linear units. The refer to problem find the best set of input values a given trained in order produce predefined desired output presence constraints on values. analyzes characteristics optimal solutions activation units and proposes algorithm by exploiting their ...