Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
In this paper, we propose a new constructive method, based on cooperative coevolution, for designing automatically the structure of a neural network for classification. Our appro...
In recent years Neural Networks have been widely used as pattern and statistical classifiers in bio medical engineering. Most research to date using hybrid systems (Fuzzy-Neuro) fo...
we present a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). We apply it to learning and recognition of situations composed of objects. NM...
Abstract--Manufacturing scheduling is an important but difficult task. In order to effectively solve such combinatorial optimization problems, this paper presents a novel Lagrangia...