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Developing mathematical model of a process or system from experimental data is known as empirical modeling. Traditional mathematical techniques are unsuitable to solve empirical modeling problems due to their nonlinearity and multimodality. So, there is a need of an artificial expert that can create model from experimental data. In this paper, we explored the suitability of Neural Network (NN) and...
As a branch of artificial intelligence, Artificial neural network is an emerging discipline which has been developing rapidly in recent years. With the gradual deepening research, this theory has infiltrated into biology, electronics, mathematics, physics, construction engineering and other disciplines, and enjoys extensive application prospects. In this paper, working principles of artificial neural...
The imitation bat detection system, which is according to principles of bat binaural echolocation, is made up of a imitation bat mouth, two imitation bat ears and a central controller. One of the key problems in the central controller, which make decisions with adaptive environment, is how to establish effective localization methods. To realize the rapid and effective localization, the information...
In artificial Spiking Neural Networks (SNNs) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. It is strongly desired to derive efficient learning methods of SNNs. It is, however, much more difficult to analyze...
An applicable time-delay globally coupled map model using the symmetric map (TDSG) is considered. Firstly, its rich dynamic behaviours are exhibited. Secondly, a parameter modulated control as a type of adaptive control method is applied for associate memory, and then the dynamics of the controlled system with time delay d = 1 is elaborated. By means of the control method, the output of the TDSG runs...
This paper presents an approach for time series forecasting using a new class of fuzzy neural networks called uninetworks. Uninetworks are constructed using a recent generalization of the classic and and or logic neurons. These generalized logic neurons, called unineurons, provide a mechanism to implement general nonlinear processing and introduce important characteristics of biological neurons such...
According to dynamic information processing problems concerning process fuzzy information or domain rules, this paper presents a weighted fuzzy reasoning process neuron and a weighted fuzzy reasoning process neural network model. The weighted fuzzy reasoning process neuron uses dynamic information processing methods of fuzzy process reasoning rules and numerical process neuron in combination, and...
Keyword clustering is useful for text information retrieval, text document classification and so on. This paper introduces an unsupervised method to cluster Chinese keyword by the artificial neural network of SOM (self-organized map). Keywords are encoded into numeric vectors by the similarities of their contextual word sets, which are composed by their neighbor words in the range of phrases. The...
Neural network is a type of network that carries out information processing through the interaction of neurons. The storage of knowledge and information shows distributed physical connection of mutual-linking network components. Bi-directional associative memories (BAM) neural network is a type of feedback neural network system of bi-directional stability, which exists simple characteristics that...
Biological brains are dramatically more effective in dealing with real-world adaptive information processes and decisions than most advanced computers. Advanced computers can utilize the discipline of classical signal processing whereby providing theoretical mathematical and statistical approaches for information processing, and with the vision of bio-inspired adaptive processing are evolving into...
The complex-signum function has been widely used as an activation function in complex-valued recurrent neural networks for multistate associative memory. This paper presents two alternative activation functions with circularity. One is the complex-sigmoid function based on a multilevel sigmoid function defined on a circle. The other is a characteristic of a bifurcating neuron represented by a circle...
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