Because of the limitation of the expert system which is based on the rule,this text proposes introducing the neural network technology into the fault diagnosis *** we recommend the frame and the principle of the exper...
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Because of the limitation of the expert system which is based on the rule,this text proposes introducing the neural network technology into the fault diagnosis *** we recommend the frame and the principle of the expert *** at last,we complete the simulation experiment which indicates the rationality of this desin..
The system uses nRF2401 for short-range wireless communications,GPRS for long-distance wireless communications,ARM9 for center console,Wireless Multi-sensor Fire Detector for node,and bp algorithm is used for judging ...
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The system uses nRF2401 for short-range wireless communications,GPRS for long-distance wireless communications,ARM9 for center console,Wireless Multi-sensor Fire Detector for node,and bp algorithm is used for judging the probability of *** Multi-sensor Fire Detector is formed of the low-power electrochemical carbon monoxide sensor,photoelectric smoke detector and semiconductor temperature *** algorithm program is embedded in the S3C2440A *** samples of bp algorithm were derived from the fire detection standard room of the State Key Laboratory of Fire Science of *** console uses Em GIS(embedded GIS) to show where the fire break out and uses GPRS to transmit SMS to the fire command *** system is low false alarm rate,low cost,fast response and convenient to install.
As a key technology of intelligent transportation system, higher identification accuracy, robustness and real-time are needed in vehicle recognition. Therefore, in view of the features of vehicle types, this paper pro...
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As a key technology of intelligent transportation system, higher identification accuracy, robustness and real-time are needed in vehicle recognition. Therefore, in view of the features of vehicle types, this paper proposes a bp neural network car types classifier method based on fuzzy C-means clustering. First, on the basis of the pretreatment of the images of the vehicle ,we abstract the features of car types from images and classify the massive dataes by fuzzy C- means clustering algorithm. Then, design the bp neural networks to train and test the classified data. Finally it is carried on compressive judgment by the computer. Experiments prove the validity of the classifier. It can recognize the highway vehicle types rapidly.
A uniformly most powerful(UMP) belief propagation(bp) based algorithm is referred to as a simplified version of a bp algorithm with reduced complexity but performance loss for low density parity check(LDPC) *** compen...
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A uniformly most powerful(UMP) belief propagation(bp) based algorithm is referred to as a simplified version of a bp algorithm with reduced complexity but performance loss for low density parity check(LDPC) *** compensate the performance loss,the normalized bp based algorithm was proposed,where the normalization factor was derived by mean ratio or by minimizing the mean square *** this paper,an improved novel normalized bp based algorithm is *** normalization uses multiplicative factor instead of divisional *** novel scheme shows better performance than the existing normalized bp based algorithms while keeping the same implementation *** simulation is done for two kinds of LDPC codes:random constructed codes and finite geometry codes. At high signal-to-noise ratio(SNR) region,the proposed scheme can achieve even better performance than bp algorithm for short length random constructed LDPC codes.
Low-density parity-check (LDPC) codes deliver very good performance when decoded with the bp algorithm. Unfortunately, this algorithm has the disadvantage to be complex for implementation. In this paper, we investigat...
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ISBN:
(纸本)9781424416875
Low-density parity-check (LDPC) codes deliver very good performance when decoded with the bp algorithm. Unfortunately, this algorithm has the disadvantage to be complex for implementation. In this paper, we investigate the bp algorithm and its simplifications (bp-Based, normalized bp-Based, offset bp-Based). Previous works on normalized and offset bp-Based proposed theoretical values for the offset and normalization factors, we perform simulations which allow to validate these theoretical results.
The system uses nRF2401 for short-range wireless communications, GPRS for long-distance wireless communications, ARM9 for center console, Wireless Multi-sensor Fire Detector for node, and bp algorithm is used for ...
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ISBN:
(纸本)9781424438631;9781424438624
The system uses nRF2401 for short-range wireless communications, GPRS for long-distance wireless communications, ARM9 for center console, Wireless Multi-sensor Fire Detector for node, and bp algorithm is used for judging the probability of fire. Wireless Multi-sensor Fire Detector is formed of the low-power electrochemical carbon monoxide sensor, photoelectric smoke detector and semiconductor temperature sensor. bp algorithm program is embedded in the S3C2440A ARM. The samples of bp algorithm were derived from the fire detection standard room of the State Key Laboratory of Fire Science of China. Center console uses Em GlS(embedded GIS) to show where the fire break out and uses GPRS to transmit SMS to the fire command center. The system is low false alarm rate, low cost, fast response and convenient to install.
The fuzzy controller with neural network is proposed in this paper, this system consists of a fuzzy neural network controller and a model identification network, the fuzzy neural network controller is optimized by the...
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ISBN:
(纸本)9781424425020
The fuzzy controller with neural network is proposed in this paper, this system consists of a fuzzy neural network controller and a model identification network, the fuzzy neural network controller is optimized by the offline genetic algorithm of global searching ability, and the online bp algorithm ability of local searching. The detailed control algorithm is given, the algorithm of system identification based on modified Elman network is ratiocinated. The simulation result shows the feasibility and validity of the proposed method.
It is difficult to learn TSK fuzzy model because the problem is multi-constraint and multi-target optimization. GA-bp hybrid learning method for the model is proposed. Some problems related to a species coding means f...
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ISBN:
(纸本)9781424441983
It is difficult to learn TSK fuzzy model because the problem is multi-constraint and multi-target optimization. GA-bp hybrid learning method for the model is proposed. Some problems related to a species coding means for the model structure, evolution and fitness evaluation strategy are discussed. The error back propagation algorithm (bp) for training the antecedent and consequent parameters during the process of evolution is inferred. The characteristic Of the method requests a little of previous information about objects, and avoids slow convergence, and has better adaptive capability, and is able to obtain compact and accurate fuzzy model from samples, the validity of the method has been demonstrated by an example of function approximation.
Feed forward neural Network (FNN) has been widely applied to many fields because of its ability to closely approximate unknown function to any degree of desired accuracy. Back Propagation (bp) is the most general lear...
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Feed forward neural Network (FNN) has been widely applied to many fields because of its ability to closely approximate unknown function to any degree of desired accuracy. Back Propagation (bp) is the most general learning algorithms, but is subject to local optimal convergence and poor performance even on simple problems when forecasting out of samples. Thus, we proposed an improved Bacterial Chemotaxis Optimization (BCO) approach as a possible alternative to the problematic bp algorithm, along with a novel adaptive search strategy to improve the efficiency of the traditional BCO. Taking the classical XOR problem and sinc function approximation as examples, comparisons were implemented. The results demonstrate that our algorithm is obviously superior in convergence rate and precision compared with other training algorithms, such as Genetic algorithm (GA) and Taboo Search (TS).
Artificial Neural Networks (ANN) deal with information through interactions among neurons (or nodes), approximating the mapping between inputs and Outputs based oil nonlinear functional composition. They have the adva...
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Artificial Neural Networks (ANN) deal with information through interactions among neurons (or nodes), approximating the mapping between inputs and Outputs based oil nonlinear functional composition. They have the advantages of self-learning self-organizing, and self-adapting. It is practical to use ANN technology to carry out hydrologic calculations. To this end, this note has fundamentally set Lip a system of calculation and analysis based oil ANN technology, and given all example of application with good results. It shows that ANN technology provides a relatively effective way of solving problems in hydrologic calculation. (C) 2007 Elsevier Ltd. All rights reserved.
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