Data Set Used
Prototype-based rules are an interesting alternative to fuzzy and crisp logical rules, in many cases providing simpler, more accurate and more com-prehensible description of the data. Such rules may be directly converted to fuzzy rules. A new algorithm for generation of prototype-based rules is introduced and a comparison with results obtained by neurofuzzy… (More)
The presented paper describes a method of knowledge extraction that is based on analysis of the trained ANN's weights The method allows to determine the significance of particular inputs, to prove their synergy as well as to find some symbolic rules, that determine the direction of influence of particular inputs.
This paper presents the application of neural networks in the design process of new technologies taking into account factors such as their influence on the environment and the economic effects of their implementation. The use of neural networks allowed eco-efficiency assessment of technologies based on highly reduced number of descriptive design parameters,… (More)
This paper presents a neural network tree regression system with dynamic optimization of input variable transformations and post-training optimization. The decision tree consists of MLP neural networks, which optimize the split points and at the leaf level predict final outputs. The system is designed for regression problems of big and complex datasets. It… (More)
In this paper we compare different evolutionary algorithm approaches and parameters used to optimize the output of neural network committee trained on regression problems. This is especially useful for large and complex datasets. We used the methodology presented in this paper to optimize the output of the committee to predict the temperature in the… (More)
Classical training methods of computational intelligence models are based on building a knowledge base, assuming that the entire, complete set of learning vectors is available. This assumption is not always met, particularly in issues related to the industry. In the paper we provide an overview of a broad group of algorithms supporting incremental learning… (More)
This paper presents regression models based on an ensemble of neural networks trained on different data that negotiate the final decision using an optimization approach based on an evolutionary approach. The model is designed for big and complex datasets. First, the data is clustered in a hierarchical way and then using different level of cluster and random… (More)