The mapping capability of artificial neural networks (ANN) is dependent on their structure, ie,
the number of layers and the number of hidden units. There is no formal way of computing …

In this paper, a proposed algorithm that dynamically changes the neural network structure is
presented. The structure is changed based on some features in the cascade correlation …

In this introduction and review—like in the book which follows—we explore the hypothesis
that adaptive growth is a means of producing brain-like machines. The emulation of neural …

The nested generalized exemplar theory accomplishes learning by storing objects in
Euclidean n-space, as hyperrectangles. Classification of new data is performed by …

Abstract Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems
of local cooperation between agents. We present smapy, an ensemble based AMAS …

Reviewing old ones is to better understand new ones and also for innovating. The mapping
capability of artificial neural networks is dependent on their structure, ie, the number of …

The generalization ability of artificial neural networks (ANN) is highly dependent on their
architectures and can be critical to solving a given problem. The current best practice uses …

When employing constructive algorithms, deciding when to add a new hidden unit is an
important challenge to be considered. The most common method is if the error stops …

Artificial Neural Networks (ANNs) are one of the most widely used form of machine learning
algorithms. Over the years numerous types of ANN have been developed and applied to …

This paper presents cascading neural networks using adaptive sigmoidal function
(CNNASF). The proposed algorithm emphasizes on architectural adaptation and functional …