Training product unit neural networks with genetic algorithms

Training Product Unit Neural

Product Unit Neural Networks with

It has remained an open question whether there exist product unit networks with constant depth that have superlinear VC dimension. In this paper we give an answer by constructing two-hidden-layer networks with this property. We further show that the pseudo dimension of a single product unit is linear. These results bear witness to the cooperative eeects on the computational capabilities of prod...

Training Feedforward Neural Networks Using Genetic Algorithms

Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization p...

Training Artificial Neural Networks with Genetic Algorithms for Stock Forecasting

Accurate prediction of future stock market prices is of great importance to traders. The process can be automated using artificial neural networks. However, the conventional backward propagation of errors algorithm commonly used for training the networks suffers from the local minima problem. This study investigates whether investing more computational resources into training an artificial neur...

Memetic Algorithms to Product-Unit Neural Networks for Regression

In this paper we present a new method for hybrid evolutionary algorithms where only a few best individuals are subject to local optimization. Moreover, the optimization algorithm is only applied at specific stages of the evolutionary process. The key aspect of our work is the use of a clustering algorithm to select the individuals to be optimized. The underlying idea is that we can achieve a ve...