A type of Mamdani interval type-2 fuzzy logic systems is designed for historical data based forecasting problem in the paper. In the Mamdani interval type-2 fuzzy logic systems design, the antecedent, consequent, and ...
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A type of Mamdani interval type-2 fuzzy logic systems is designed for historical data based forecasting problem in the paper. In the Mamdani interval type-2 fuzzy logic systems design, the antecedent, consequent, and input measurement primary membership functions of type-2 fuzzy sets are chosen as Gaussian type-2 membership functions with uncertain standard deviation. Some excellent elementary vectors and partitioned matrices are used to combine Karnik-Mendel (KM) algorithms with back propagation (bp) algorithms by matrix transformation, and the challenging mission of computing derivatives in such systems can be solved. The parameters of the proposed type-2 fuzzy logic systems are also tuned. Two examples, including the historical competition data of European Network on Intelligent Technologies (EUNITE) (three o'clock from January 1, 1997 to December 9, 1998) and the price data of West Texas Intermediate (WTI) crude oil (from January 3, 2011 to December 30, 2011) are used to test traditional linear time series forecasting methods and more advanced fuzzy logic systems forecasting methods. Monte Carlo simulation studies and convergence analysis are employed to illustrate the effectiveness of the proposed type-2 fuzzy logic systems methods compared with their type-1 counterparts methods for forecasting. (C) 2015 Elsevier B.V. All rights reserved.
The process of permanent magnetic drive (PMD) presents high uncertainty under the complex operating conditions. In this paper, a type of Takagi Sugeno Kang (TSK) interval type-2 fuzzy logic systems (IT2 FLSs) under th...
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The process of permanent magnetic drive (PMD) presents high uncertainty under the complex operating conditions. In this paper, a type of Takagi Sugeno Kang (TSK) interval type-2 fuzzy logic systems (IT2 FLSs) under the Karnik-Mendel (KM) structure is designed for data-based PMD torque and revolutions per minute (rpm) forecasting. For designing the antecedent and input measurement of TSK IT2 FLSs, the primary membership functions (MFs) of interval type-2 fuzzy sets (IT2 FSs) are all selected as Gaussian type-2 MFs with uncertain derivation, while the consequent parameters are chosen as type-1 fuzzy numbers. According to matrix transformation, the complicated task of calculating derivatives in the TSK IT2 FLSs under the Karnik-Mendel structure can be managed subtly by some elementary vectors and partitioned matrices. And the parameters of the proposed systems are also tuned by the back propagation (bp) algorithms. Simulation examples based on the data of PMD torque and rpm are used to test the advanced fuzzy logic systems forecasting methods. The effective and feasibility of forecasting by the proposed type-2 systems compared with their type-1 counterparts is illustrated in the light of Monte Carlo simulations, convergence and stability analysis.
At present, statistical process control in process industry is mainly off-line, which is incompetence in handling problems such as process control deviations and quality instability. In this work, an intelligent proce...
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Pressure data acquired from a blood pressure cuff during a blood pressure test are the starting point of noninvasive, single cuff blood pressure determination. Most blood pressure devices on the market use the single ...
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ISBN:
(纸本)9788026107224
Pressure data acquired from a blood pressure cuff during a blood pressure test are the starting point of noninvasive, single cuff blood pressure determination. Most blood pressure devices on the market use the single cuff method. This paper describes acquisition, analog and digital processing, and storage of raw data from a blood pressure cuff during gradual cuff deflation. Raw data from 30 arm cuff tests and 20 wrist cuff tests were organized to form a database of text files. Separate annotation documents were also created. The database was uploaded to the Google Drive. The database can be downloaded by any interested party. The data can be used for the development of algorithms for blood pressure determination and for the assessment of some cardiovascular variables.
Based on the data of the rainfall from 24 base stations on the area of Chao River Basin in the near 54 years(range from 1958 to 2012), the estimation is carried out by using Arcgis kriging interpolation and bp algorit...
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Based on the data of the rainfall from 24 base stations on the area of Chao River Basin in the near 54 years(range from 1958 to 2012), the estimation is carried out by using Arcgis kriging interpolation and bp algorithms. And try to do the error analysis and the consequences comparing of these two results in the use of statistical approach. It is surely an innovative study which applies the advanced mathematical method in the rainfall research in the environmental field. After a serious of complicated data processing, the final conclusion is that it is feasible to apply bp neural network model to the forecast of rainfall, which is the correct method with high accuracy rate.
This paper put forward the realization of the self-automation role, which has leaning ability and dynamical acclimatization. First of all, bp algorithm of artificial neural net(ANN) is improved, the self-adjusted algo...
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ISBN:
(纸本)9780769538655
This paper put forward the realization of the self-automation role, which has leaning ability and dynamical acclimatization. First of all, bp algorithm of artificial neural net(ANN) is improved, the self-adjusted algorithm of all parameters has been proposed for the back-propagation learning, which can make the selection of hidden layer units and rate of studying easily in the course of training, reduce artificial influence and improve the adaptive ability of rate of studying and neural net. Secondly, Genetic algorithms (GA) has been optimized from primitive colony, selective manipulation, intercross manipulation. At the same time, methodlogy of ANN was integrated with GA and self-learning models of NPC were created to control their behaviors. At last, the experimental results have shown that self-learning system of NPC provides artificial behaviors with more automation and intelligence.
Low-density parity-check (LDPC) codes are one of the most powerful error correcting codes and are attracting much attention these days. LDPC codes are promising for communications and broadcasting as well where the us...
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Low-density parity-check (LDPC) codes are one of the most powerful error correcting codes and are attracting much attention these days. LDPC codes are promising for communications and broadcasting as well where the use of error correcting codes are essential. LDPC codes have been standardized in some communication standards, such as, IEEE802.16e, DVB-S2, IEEE802.3an (10BASE-T), and so on. The performance of LDPC codes largely depend on their code structure and decoding algorithm. In this paper, we present the basics of LDPC codes and their decoding algorithms. We also present some LDPC codes that have good performance and are receiving much attention particularly in communication systems. We also overview some standardized LDPC codes, the LDPC codes standardized in DVB-S2 and the IEEE802.16e standard LDPC codes. Moreover, we present some research on LDPC coded MIMO systems and HARQ using LDPC codes.
Aiming at the disadvantages of bp model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...
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Aiming at the disadvantages of bp model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and bp algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
The large-scale analogue circuit fault detection and diagnosis is a difficult task. Aiming at the limitation of diagnosis technology in tradition, a fault detection and diagnosis system based on neural network is desi...
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The large-scale analogue circuit fault detection and diagnosis is a difficult task. Aiming at the limitation of diagnosis technology in tradition, a fault detection and diagnosis system based on neural network is designed, and its result shows that this method is effective. This method is so promising and widely applicable that it provides a new theoretic proof and detecting approach. Moreover, the design idea, realizing and a concrete diagnosis example are introduced.
In order to make good use of the ability to approach any function of bp (backpropagation) network and overcome its local astringency, and also make good use of the overallsearch ability of GA (genetic algorithms), a p...
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In order to make good use of the ability to approach any function of bp (backpropagation) network and overcome its local astringency, and also make good use of the overallsearch ability of GA (genetic algorithms), a proposal to regulate the network's weights using bothGA and bp algorithms is suggested. An integrated network system of MGA (mended genetic algorithms)and bp algorithms has been established. The MGA-bp network's functions consist of optimizing GAperformance parameters, the network's structural parameters, performance parameters, and regulatingthe network's weights using both GA and bp algorithms. Rolling forces of 4-stand tandem cold stripmill are predicted by the MGA-bp network, and good results are obtained.
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