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An architecture and learning methods for a growing neuro-fuzzy system that enlarges an amount of layers and tunes their synaptic weights in an online way are introduced in the paper. A structure of the hybrid system is built with the help of extended neo-fuzzy neurons which are characterized by improved approximating capabilities. The main peculiar feature of the introduced system is a learning method...
An architecture and learning methods for deep neural networks that increase a number of layers and adjust their synaptic weights in an online mode are proposed in the article. The system's architecture is based on nodes of a special type (extended neo-fuzzy neurons) which possess enhanced approximating properties. A main feature of the proposed network is a learning process for each node that is performed...
In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on Group Method of Data Handling approach and Least Squares Support Vector Machines with fixed number of the synaptic weights. The proposed system is simple in computational implementation, characterized by high learning...
In this paper we propose architecture of hybrid generalized additive neuro-fuzzy system. Such system is hybrid of the neuro-fuzzy system of Wang-Mendel and the generalized additive models of Hastie-Tibshirani. Proposed hybrid generalized additive neuro-fuzzy system can be used for solving different tasks of computational intelligence and data stream mining. The results of experimental modelling confirm...
In the presentation major difficulties of designing neural networks are shown. It turn out that popular MLP (Multi Layer Perceptron) networks in most cases produces far from satisfactory results. Also, popular EBP (Error Back Propagation) algorithm is very slow and often is not capable to train best neural network architectures. Very powerful and fast LM (Levenberg- Marquardt) algorithm was unfortunately...
In nature, multiple agents in teams collaborate and compete with one another at the same time. Replicating such agent interactions in games can make for realistic opponent teams. Yet cooperation and competition have mostly been studied separately so far. This paper focuses on simultaneous cooperative and competitive coevolution in a complex predator-prey domain. Multi-Agent ESP [23] architecture is...
Humans are very capable of solving many scientific and engineering problems, but during the solution process they have a tendency to make mistakes. For example, humans without computer aided tools, would not be able to design VLSI chips larger than 100 transistors. This imperfection of humans make them very unreliable elements in resilient control systems. There is a tendency of replacing humans with...
In this paper a novel neural network architecture for medium-term time series forecasting is presented. The proposed model, inspired on the Hybrid Complex Neural Network (HCNN) model, takes advantage of information obtained by wavelet decomposition and of the oscillatory abilities of recurrent neural networks (RNN). The prediction accuracy of the proposed architecture is evaluated using 11 economic...
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