The annealing algorithm
Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
The nature of statistical learning theory
The nature of statistical learning theory
A comparative study of neural network based feature extraction paradigms
Pattern Recognition Letters
Training Product Unit Neural Networks with Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A generalized feedforward neural network architecture for classification and regression
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Implementing projection pursuit learning
IEEE Transactions on Neural Networks
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An ART-based construction of RBF networks
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Regression modeling in back-propagation and projection pursuit learning
IEEE Transactions on Neural Networks
Computers and Electronics in Agriculture
Evolutionary Combining of Basis Function Neural Networks for Classification
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Computers and Electronics in Agriculture
Evolutionary product-unit neural networks classifiers
Neurocomputing
Expert Systems with Applications: An International Journal
Hybrid evolutionary algorithm with product-unit neural networks for classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
A neural network of smooth hinge functions
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Expert Systems with Applications: An International Journal
Evolutionary product-unit neural networks for classification
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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This paper presents a new method for regression based on the evolution of a type of feed-forward neural networks whose basis function units are products of the inputs raised to real number power. These nodes are usually called product units. The main advantage of product units is their capacity for implementing higher order functions. Nevertheless, the training of product unit based networks poses several problems, since local learning algorithms are not suitable for these networks due to the existence of many local minima on the error surface. Moreover, it is unclear how to establish the structure of the network since, hitherto, all learning methods described in the literature deal only with parameter adjustment. In this paper, we propose a model of evolution of product unit based networks to overcome these difficulties. The proposed model evolves both the weights and the structure of these networks by means of an evolutionary programming algorithm. The performance of the model is evaluated in five widely used benchmark functions and a hard real-world problem of microbial growth modeling. Our evolutionary model is compared to a multistart technique combined with a Levenberg-Marquardt algorithm and shows better overall performance in the benchmark functions as well as the real-world problem.