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Pujol, João Carlos Figueira; Poli, Roberto

doi:10.1023/a:1008272615525

Cited by 57 publications

References 21 publications

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Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks

Curteanu

Cartwright

2011

Journal of Chemometrics

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Artificial neural networks (ANNs) are comparatively straightforward to understand and use in the analysis of scientific data. However, this relative transparency may encourage their use in an uncritical, and therefore possibly unproductive, fashion. The geometry of a network is among the most crucial factors in the successful deployment of network tools; in this review, we cover methods that can be used to determine optimum or near-optimum geometries. These methods of determining neural network architecture include the following: (i) trial and error, in which architectures chosen semirandomly are tested and modified by the user; (ii) empirical or statistical methods, in which an ANN's internal parameters are adjusted based on the model's performance; (iii) hybrid methods, such as fuzzy inference; (iv) constructive and/or pruning algorithms, that add and/or remove neurons or weights from an initial architecture, respectively, based on a predefined link between architecture and ANN performance; (v) evolutionary strategies, which search the topology space using genetic operators to vary the neural network parameters. Several case studies illustrate the development of neural network models for applications in chemistry and chemical engineering.

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Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks

Curteanu

Cartwright

2011

Journal of Chemometrics

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Neuroevolution with Analog Genetic Encoding

Dürr

Mattiussi

Floreano

2006

Parallel Problem Solving From Nature - PPSN IX

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Abstract. The evolution of artificial neural networks (ANNs) is often used to tackle difficult control problems. There are different approaches to the encoding of neural networks in artificial genomes. Analog Genetic Encoding (AGE) is a new implicit method derived from the observation of biological genetic regulatory networks. This paper shows how AGE can be used to simultaneously evolve the topology and the weights of ANNs for complex control systems. AGE is applied to a standard benchmark problem and we show that its performance is equivalent or superior to some of the most powerful algorithms for neuroevolution in the literature.

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Designing Neural Networks Using Gene Expression Programming

Ferreira¹

Advances in Soft Computing

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Cited by 57 publications

References 21 publications

Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks

Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks

Neuroevolution with Analog Genetic Encoding

Designing Neural Networks Using Gene Expression Programming

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