Add to ORKGAimed at the pattern classification and the system-modelling problem with complex time-varying signals that have singular values, a kind of structural formula process neural networks is proposed in this paper. This model is constructed based on the rational expression function's approximation character to complex process signals and the process neural networks' nonlinear transforming mechanism to time-varying information; its basic information processing unit is made up of two process neurons, logically constructing a structural process neuron. It is a type of extending for artificial neural networks at the architecture and the information processing mechanism. In this paper the continuity and functional approximation ability of s...
This paper reviews the model structures and learning rules of four commonly used artificial neural n...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
Monolithic neural networks may be trained from measured data to establish knowl-edge about the proce...
Both the input and link weights of process neural network can be all time-various functions, an aggr...
In order to solve the problems in real systems where inputs and outputs are time-varied continuous f...
A class of process neural network model with two hidden-layer based on expansion of basis function i...
Aimed at the information process problem that the system inputs are multivariate process functions a...
A feedback process neural network model based on weight function base expansion is put forward. Stru...
Process neural network is widely used in modeling temporal process inputs in neural networks. Tradit...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Aim at the problems that the inputs and outputs of some practical nonlinear systems are Continuous t...
In this paper, we proposed an optimized process neural network based on fourier orthogonal base func...
A new structure of neural network based systems for modeling and control of dynamic industrial proce...
This paper reviews the model structures and learning rules of four commonly used artificial neural n...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
Monolithic neural networks may be trained from measured data to establish knowl-edge about the proce...
Both the input and link weights of process neural network can be all time-various functions, an aggr...
In order to solve the problems in real systems where inputs and outputs are time-varied continuous f...
A class of process neural network model with two hidden-layer based on expansion of basis function i...
Aimed at the information process problem that the system inputs are multivariate process functions a...
A feedback process neural network model based on weight function base expansion is put forward. Stru...
Process neural network is widely used in modeling temporal process inputs in neural networks. Tradit...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Monolithic neural networks may be trained from measured data to establish knowledge about the proces...
Aim at the problems that the inputs and outputs of some practical nonlinear systems are Continuous t...
In this paper, we proposed an optimized process neural network based on fourier orthogonal base func...
A new structure of neural network based systems for modeling and control of dynamic industrial proce...
This paper reviews the model structures and learning rules of four commonly used artificial neural n...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
Monolithic neural networks may be trained from measured data to establish knowl-edge about the proce...