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Granular computing is the key to granular neural networks, and in fact it is also the main problem in knowledge discovery and data mining. This paper addresses fuzzy information extraction and granular computing in granular neural networks in order that fuzzy rules can be discovered from fuzzy information which is difficult to be measured accurately with numerical data and furthermore the missing...
To improve the accuracy and reliability of deformation monitoring data on analyzing and forecasting, this paper attempts to apply the method that combines fuzzy logic and neural network in deformation monitoring data process, and predict with other methods accordingly, then we come to a conclusion through the comparison of instance data that Fuzzy Neural Network for deformation monitoring forecasting...
The principle and step of performance evaluation of project management based on fuzzy rules and wavelet neural network are studied. The index system of performance evaluation of project management is set up. Then we built up the evaluation model on fuzzy rules and wavelet neural network. Finally, take some samples of project for an example, we carry on this model to instance. It can take a preferably...
The principle and step of performance evaluation of project management based on SVM and fuzzy rules are studied. The index system of performance evaluation of project management is set up. Then we built up the evaluation model on SVM and fuzzy rules. Finally, take some samples of project for an example, we carry on this model to instance. It can take a preferably evaluation, so that it is a viable...
In this paper, based on the traditional algorithm of TSK fuzzy reasoning model, a new fuzzy reasoning algorithm is proposed for two rules, two linguistic input variables and one output variable, in which the membership functions are Gaussian-type functions. By using neural network back-propagation algorithm, the parameters in the membership functions can be adjusted on-line without changing the rules...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human...
In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) is presented for detection of erythemato-squamous diseases. The domain contained records of patients with 34 features and known diagnosis of six disease indications. Given a training set of such records, the CANFIS classifiers learned how to differentiate a new case in the domain that may be difficult even for experienced...
In this paper, we compare two soft computing methods used for product filtering in web personalisation for E-commerce. Due to the diversely behaving nature, and the complexity to model the customers' behaviour using market research methodologies, it is difficult to build a universal model relating the purchasing behaviour mathematical in E-commerce. For this reason, soft computing techniques may be...
Today, it's the need of developed and developing countries to consume electricity more efficiently. Though developed countries do not want to waste electricity and developing countries cannot waste electricity. Hence, the wise use of electricity is the need of hour. This leads to the concept - load forecasting. This paper is written for the short term load forecasting on daily basis. Though this can...
This paper presents the development and design of two software tools for computational intelligence. The software tools include a graphical user interface for construction, edition and observation of the intelligent systems. The software tool are for interval type-2 fuzzy logic and modular neural networks. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2...
Information retrieve is one of the most important operations in computer information systems. This paper presents a kind of fuzzy information retrieve method based on soft computing (SCFIR for short). SCFIR adopts fuzzy clustering analysis and artificial neural networks to organize databases in systems so as to increase the efficiency of fuzzy retrieve. At the same time, SCFIR realizes the understanding...
In this paper, an automated fault diagnosis system essentially based on neural networks and fuzzy logic, in a hybrid scheme, is suggested. First, a signal classification and image classification, resulting in a signal diagnosis and image diagnosis respectively, are developed. Such dual-classification is then exploited in a fuzzy system 1 to ensure a satisfactory reliability to medical diagnosis and...
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