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