Structure evolution for time-delay neural networks

The paper presents a structure finding algorithm for time-delay neural networks based on the working principle of evolutionary algorithms. Multilayer perceptrons, which are a subclass of time-delay neural networks, can also be constructed. The algorithm selects appropriate input features for the neural networks from a set of possible inputs, finds optimal values for the number of layers and hidden neurons, constructs connections between neurons, and determines the ideal values of time-delays. The approach uses a new, graphical coding scheme, a rank-based selection mechanism, and seventeen reproduction operators for mutation and crossover. The advantages of this approach are shown by means of an application example (tool wear estimation in turning)