Adaptive Structural Optimisation of Neural Networks

Fitness evaluation for structural optimisation genetic algorithms using neural networks

This paper relates to the optimisation of structural design using Genetic Algorithms (GAs) and presents an improved method for determining the fitness of genetic codes that represent possible design solutions by using a neural network to generalize fitness. Two problems that often impede design optimization using genetic algorithms are expensive fitness evaluation and high epistasis. In this pa...

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

We present a new theoretical framework for analyzing and learning artificial neural networks. Our approach simultaneously and adaptively learns both the structure of the network as well as its weights. The methodology is based upon and accompanied by strong data-dependent theoretical learning guarantees. We present some preliminary results to show that the final network architecture adapts to t...

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

There have been several major lines of research on the theoretical understanding of neural networks. The first one deals with understanding the properties of the objective function used when training neural networks (Choromanska et al., 2014; Sagun et al., 2014; Zhang et al., 2015; Livni et al., 2014; Kawaguchi, 2016). The second involves studying the black-box optimization algorithms that are ...

Adaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks

This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the contro...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)