The predicted value is a linear combination of the known training values, in a probabilistic

neural network approach.

Radial basis function (RBF)

neural network is able to be used in logistics requirement prediction, and RBF

neural networks is developed based on the radial basis functions.

With conventional algorithmic methods, learning complex relationships between input and output patterns would be very difficult and MLP has some advantage over conventional methods (Hwarng 2001) and

neural network is a much better tool than regression modelin cross-validation multivariate correlations and correct classifications (Collins, Clark 1993).

In previous works [11, 12] it is shown the feasibility and efficiency of usage of

neural network based on radial basis functions.

Numerical experiments were performed using the following types of

neural networks: generalized regression

neural network (GR NN), linear

neural network, radial basis

neural network with a minimal number of neurons (RB NN MMN), radial basis

neural network with zero error (RB NN ZE).

Theorem 2 can be derived from the above analysis: if suppose assumption 1) and 2) set up, then nonlinear

neural network (6) exists the only balance point which is globally asymptotically stable.

Before creating a

neural network and processing data, the scenario of the required application has to be well considered.

In the second

neural network model (N2) only the two parameters that had significant contribution to linear regression models were included.

MPC Algorithms with Nonlinear Optimization (MPC-NO) and

Neural Network Models

0, which works through an Excel interface, was used for generation of the

neural network.

Thus, we incorporate

neural network learning concepts in fuzzy inference systems, resulting in fuzzy-

neural networks (FNN) [9-11] with their greatest application in implementation of classification of fuzzy information.

APPLICATION:

Neural networks may be useful to help control moisture content and increase dryer efficiency.