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The most important challenge in Wireless Sensor Networks (WSNs) is to improve the operational efficiency in highly resource constrained environment based on dynamic and unpredictable behaviour of network parameters and applications requirement. In this paper we have proposed a method for clustering and their analysis to study the cluster formation, their behaviour with respect to the system parameters...
In this paper, a neural architecture which gives identical TSK fuzzy system is proposed based on the area selection concept in neural network design. Instead of using traditional membership functions for selection the range of operation, the monotonic pair-wire or sigmoidal activation function is used. In the comparison to popular neuro-fuzzy systems, the proposed approach does not require signal...
The failures auto-sensing becomes increasingly essential in the complex systems exploitation. This article consists in working out a system of defects diagnosis based on an artificial intelligence technique which associates fuzzy logic with neural networks. The method is applied to obtain the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). This...
In this paper, we combine computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining framework (DMFBCI), which discovers patterns and represents them in understandable forms. In the DMFBCI, input data are preprocessed by fuzzification or one-of-m coding, then, principal component analysis (PCA) is applied to reduce the dimensions of the preprocessed...
Forensics science is based on a methodology composed by a group of stages, being the analysis one of them. Analysis is responsible to determine when a data constitutes evidence; and as a consequence it can be presented to a court. When the amount of data in a network is small, its analysis is relatively simple, but when it is huge the data analysis becomes a challenge for the forensics expert. In...
In view of the defect of traditional water quality evaluation model, based on fuzzy neural network theory, a new model of fuzzy neural network (FNN) comprehensive evaluation is developed to evaluate surface water quality in Suzhou. Fuzzy neural network is a new type neural network consisting radical basis network and compete neural network, which is simple in structure, easy for training and wide...
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...
Since the complexity and variety of indoor environments, the ability of simultaneous localization and mapping for autonomous mobile robots restricted their applications. A novel approach, which is based on clustering algorithm, fuzzy logic and neural networks, is proposed, and solve simultaneous mapping and localization problem. It adopts self-organizing fuzzy neural networks to model the environment...
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,...
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 order to design an aggregate domestic load control system, a controller requires accurate predictions of load curves to make decisions about which loads should be connected to the grid. This paper presents a 24-hour load forecaster to be used by the controller. The forecaster will employ an Artificial Neural Network (ANN) structure with one input provided by a fuzzy weather controller. The use...
Vulnerability Assessment and control are some of the essential requirements for maintaining security of modern power systems, particularly in competitive energy markets. This paper presents intelligent computational techniques for vulnerability assessment of power systems and recommends preventive control measures. Accurate techniques for vulnerability assessment and control of power systems are developed...
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