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In this paper we describe an evolutionary method for the optimization of a modular neural network for multimodal biometry. The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions) as fuzzy integration methods. The integration of responses in the modular neural network is...
Aiming at modeling and controlling a kind of nonlinear dynamic systems and dealing with the uncertainties coursing by the changing of modeling parameters, a Dynamic Fuzzy Neural Intelligent Controller (DFNIC) is presented in this paper. A dynamic fuzzy neural networks (DFNN) with a PID controller are integrated in DFNIC, in which the structure and parameters are adjusted online, and the fuzzy rules...
A system structure for water jet cutting machine fault diagnosis based on multi-information fusion is presented, which takes the time-varying, redundancy and uncertainty of the multi-fault characteristic information into consideration. We make use of the neural network's ability of better fault tolerance, strong generalization capability, characteristics of self-organization, self-learning, and self-adaptation,...
The output of the fuzzy neural network was adopted as BPAF (basic probability assignment function) in this paper. By training the fuzzy neural network, the massive language fuzzy information and the concerned expert's experience were integrated in the decision process, it advantageous in enhancing the BPAF of the accuracy, the reliability and the objectivity. Therefore, using the superiority of D-S...
Applicant selection and ranking methods for job roles within human resources (HR) systems involve high levels of uncertainty. This is due to the requirement to allow for the varying opinions and preferences of the different occupation domain experts in the decision making process. Hence, there is a need to develop novel systems that will enable HR departments to determine the most important requirements...
Research on the good and bad points of fuzzy theory and neural network technology in fault diagnosis. Combine them in this paper with the method connected. Firstly using fuzzy method to process the information, and then diagnosis of the faults with neural networks approaching ability. Construct inference system to solve complicated faults happened in gasoline engine with this method. The results of...
For the affine nonlinear system having characteristics of differential relations between states, an adaptive dynamic recurrent fuzzy neural network (ADRFNN) taking only some measurable states as its inputs and describing the system's inner dynamic relation by its feedback matrix was proposed to control the system, adaptive laws of the adjustable parameters and the evaluation errors' bounds of ADRFNN...
Type-1 fuzzy system is able to provide an inference mechanism to reason with imprecise information, but it is unable to do so under linguistic and numerical uncertainties. While the incorporation of interval type-2 fuzzy set can offer a model for handling further uncertainty, it is relatively difficult to extract the footprint of uncertainty information. In addition, fuzzy systems are unable to automatically...
Many practical problems are characterized as decision making with multiple, conflicting and noncommensurable nonlinear objectives and complex criteria. Especially in the practice of purchasing decision making, many quantitative and qualitative factors must be considered, as well as the vagueness and imprecision among them, which makes the decision process more complicated and unstructured. For identifying...
In the multi-attribute decision making theory (MADM), fuzzy multi-attribute decision making theory is an important aspect. And in practical problem, some index can't be expressed by certain number, just only expressed by linguistic words, under this situation, we must use fuzzy decision making method. This paper uses fuzzy LMS neural network to determinate weight, this method has the advantage of...
This paper firstly elaborates the technological innovation and the characteristics of risks of Chinese small & medium-sized enterprises (SMEs) , then analyzes the risk factors of SMEs' technological innovation, on the basis of which the index system of risk evaluation is constructed. Then, incorporating the BP neural network, it proposes the risk evaluation model based on fuzzy neural network...
Biological systems are slow, wide and messy whereas computer systems are fast, deep and precise. Fuzzy neural networks use fuzzy logic to implement higher level reasoning and incorporate expert knowledge into the system while neural networks deal with the low level computational structures capable of learning and adaptation. Whereas the first 2 generations of neural network are ldquorate encodedrdquo,...
This paper presents a simulation of Neuro-Fuzzy application for analysing studentspsila performance based on their CPA and GPA. The analysis is an extension of our previous study, which was called an analysis on studentpsilas performance using fuzzy systems. The main function of this analysis is to support the development of intelligent planning system (INPLANS) using fuzzy systems, neural networks,...
Interval type-2 fuzzy logic system cascaded with neural network, interval type-2 fuzzy neural system (IT2FNS), is proposed to handle complicated uncertainties in short-term traffic flow forecasting. A secondary membership function is obtained through fuzzy reasoning. The strong consistent estimates of the unknown parameters of the neural network structure are developed. The secondary membership function...
In this paper, an interval type-2 fuzzy neural network controller (IT2FNNC) is proposed. This new controller can combine the merits of neural network and type-2 fuzzy logic controller (T2FLC) which has been shown to be a powerful paradigm to handle the high level of uncertainties in real-world applications. To tune the parameters of IT2FNNC, the update rule is developed based on the backpropagation...
In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an rdquoInterval Type-2 Fuzzy Neuronrdquo, which is based on biological neural morphologies, followed by the learning mechanisms...
In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control...
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