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This study presents a methodology for on-line identification of the nonlinear reaction process in presence of measurement noise and uncertainty, for accurate simulation of this process, a link between HYSYS (chemical software) and MATLAB was generated by the author. In this link HYSYS simulates the continuous stirred tank reactor (CSTR) and MATLAB performs the data acquisition algorithm. The chemical...
In this paper, through combining information diffusion principle and BP neural network theory, a new prediction model of drought disaster assessment is established. First, the original data are fuzzily processed based on information diffusion method, then a new training sample is formed; second, the new sample is used to design and train BP neural network; finally, the trained fuzzy neural network...
In this paper, the hyperspectral data, foliar chlorophyll content and heavy metal contents in foliar and soil were measured for the crop growing in three natural farmlands by measuring outdoors and indoor sample analyzing. Three indices including NDVI, TVI/MSAVI and MCARI/MSAVI, which were sensitive to crop growth circumstance, became importation parameters, with crop contamination stress level as...
Since the excellent performances of treating nonlinear data with self-learning capability, the neural networks (NNs) are wildly use in financial prediction problem. But the NNs more or less suffer from the slow convergence, “black-box” i.e., it is almost impossible to analysis them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but...
At present, China's risks assessment for land consolidation projects remains at the stage of qualitative description. Based on the risks analysis and construction of corresponding assessment index for land consolidation projects, this paper discusses the application of compensative fuzzy neural network to their risks assessment. And the 140 projects accepted in Chongqing in the year of 2006 are used...
This thesis presents a fault diagnosis method based on the low, middle and high level fuzzy neural networks for the breakdown asynchronous motor according to the complex corresponding relations between the motor's fault symptoms and the fault causes. This can implement the fuzzy diagnosis for the motor fault. The thesis puts emphasis on the structure models of the new type hierarchical fuzzy neural...
The research establishes a credit evaluation model based on fuzzy neural network. It is used to do two patterns classification on the 106 listed companies of China in 2000. It selects four primary financial indexes: earning per share, net asset value per share, return on equity, and cash flow per share. By analyzing the statistical quantities of every variable of both the training samples and the...
This paper analyzes the impact of different detrending approaches on the performance of a variety of computational intelligence (CI) models. Three approaches are compared: Linear, nonlinear detrending (based on empirical mode decomposition) and first-differencing. Five representative CI methods are evaluated: Dynamic evolving neural-fuzzy inference system (DENFIS), Gaussian process (GP), multilayer...
Because of anonymity and flexibility of C2C online transaction, trust plays a critical role. On successful E-business, so it is very important to evaluate this trust degree. However there are much fuzzy and uncertainty in this evaluation process, so an evaluation approach based on Fuzzy Neural Network (FNN) is pointed out and applied to solve this problem. FNN has such advantages as multiple rule...
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 fermentation process, fuzzy neural networks (FNN) is a novel machine learning method of soft sensor modeling, while the typical algorithm of FNN is inefficient because they can not optimize fuzzy rules and has long training time. Biological parameters can be measured online in real time which is helpful for the control of process optimization. So this paper introduces the use of the particle swarm...
The tolerance and non-stability in financial indexes make changes to other sub-systems like human resources, economics, factory productions and etc. Having underling knowledge and a model to simulate such systems obtains a fine vision to estimate further and calculate hard-decision making tasks before execution like: dept from banks, cash injecting and insurance services. Using Neuro-fuzzy networks...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
For a long time, the necessary funds of entire electric power industry is fully funded by the government or mandatory financial loans due to the monopoly of the electric power industry and the government acts, and which results in the research on the electric power enterprise financing credit capacity (FCC) evaluation is lacking. The financing capacity is influenced by many factors, including the...
This paper presents a neural network classifier based on fuzzy ARTMAP with conflict-resolving strategy. The proposed model explicitly resolves overlaps among prototypes of different classes through deploying a contraction procedure in the network, therefore, improving its generalization. Compared with other existing methods, the model has the priority of intuition and no parameter tuning. The performance...
In the paper, the fuzzy neural network method is introduced into the field of enterprise performance evaluation in order to overcome the deficiencies of the traditional methods. We reference the state-owned capital performance evaluation index system, issued by The Ministry of Finance and other six ministries and commissions, to build evaluation index of this paper, propose and employ a hierarchical...
In this paper, a hybrid network consisting of a trigonometric functional link artificial neural network (FLANN) and fuzzy logic system named as functional link neural fuzzy (FLNF) model is used to predict the stock market indices. The proposed model uses a functional link neural network to the consequent part of the fuzzy rules. The consequent part of FLNF model is a non-linear combination of input...
This paper analyzes the stage of maturity that neurofuzzy systems (and soft computing in general) have recently reached and tackles the several reasons why they have not yet reached a widespread acceptance in industrial and agronomic applications, despite the good performance they can offer with a reduced design effort.
Energy indicators system may be a new strategy for evaluating sustainable development and the fuzzy neural network system may be an appropriate methodology to deal with the absence of information in evaluation of sustainable development. This study tries to combine fuzzy neural network with energy indicators system to evaluate the performance of sustainable development of several countries. The results...
Type 2 fuzzy systems have been under investigation for a while and the projection of type 2 understanding for uncertainty management onto the connectionist models -i.e. neural networks- seems an interesting field of research. This paper considers neurons having multiple bias values defining a new structure that resembles the uncertainty handling capability of type 2 fuzzy models. Such a neuron provides...
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