Adaptive beamforming can achieve better SNR by varying the weights of each of the sensors used in the *** traditional beamforming methods cannot achieve the optimal performance in beamforming because of the mismatch b...
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Adaptive beamforming can achieve better SNR by varying the weights of each of the sensors used in the *** traditional beamforming methods cannot achieve the optimal performance in beamforming because of the mismatch between the assumed array response and true array response.A radial basis function neural network algorithm has been proposed in this paper to solve this problem by turning the processing of calculating weighting of arrays to mapping *** simulation results indicate that the proposed method can adapt the weighting according to the direction of source signal automotive,and the SNR can be increased significantly with the DOA mismatch at 2 degrees.
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.
No lossless data compression method based on neural network is found before. A lossless compression method based on bp network for the long character-string of 0 and 1 is given by establishing specifi
No lossless data compression method based on neural network is found before. A lossless compression method based on bp network for the long character-string of 0 and 1 is given by establishing specifi
For fire detection and alarm system with simple function,positioning difficulties,false positive and false negative in traditional intelligent building,the fire detection and alarm systems based on intelligent neural ...
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For fire detection and alarm system with simple function,positioning difficulties,false positive and false negative in traditional intelligent building,the fire detection and alarm systems based on intelligent neural network have been *** can do integrated estimation with a variety of fire detection information detected by the microcontroller,neural network intelligent algorithm was joined in the software design,MATLAB simulation realize multiple synchronous intelligent detection,which effectively detect various types of early fire and reduce the fire.
This paper introduces control requirements, control method choosing, hardware design and software for the automatic mixture system of magnesia refractory bricks. S7-200 series of PLC are used to realize automatic cont...
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This paper introduces control requirements, control method choosing, hardware design and software for the automatic mixture system of magnesia refractory bricks. S7-200 series of PLC are used to realize automatic control for batching system. Because of complexity of automatic mixture system, conventional compensating control method can not meet the required question precisely. A kind of mixture control system, based on dynamic neural network, is designed through analyzing the influence precision factors of mixture. Simulation is studied by collecting the mixture historical data from field engineering. And the simulation results illustrate that the method can reduce the mixture error effectively.
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.
This paper presents the learning algorithm of neural network being applied to identification of the affine nonlinear system,and proposes a neural-network-based model reference adaptive control *** theorems on exponent...
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This paper presents the learning algorithm of neural network being applied to identification of the affine nonlinear system,and proposes a neural-network-based model reference adaptive control *** theorems on exponential stability of the system are proven. Simulation results show that this method is feasible.
In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surroundin...
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ISBN:
(纸本)9781479940318
In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from sensors of petrochemical industries need to be timely operated because of time sensitivity, and it is hard to achieve associated information from sensors located in production site. To this end, an OLA-Ibp (Online Learning algorithm based on Improved Back Propagation) is proposed. The adaptive structure of this algorithm is settled on-line. Meanwhile, real-time data streams are parallelly processed according to arriving time in input layer. Simulation results show that OLA-Ibp can efficiently improve learning time and accuracy rate. Finally, the adaptability of OLA-Ibp is verified in leak points prediction of petrochemical equipments from processed data.
Aiming at the problem that it is difficult for bp algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm ...
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ISBN:
(纸本)9781479905607
Aiming at the problem that it is difficult for bp algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm is presented which combines the quantum theory and is to train the process neural network. In this algorithm, the individuals are expressed with Bloch spherical coordinates of qubits. The quantum individuals are updated by quantum rotation gates, and the mutation of individuals is achieved with Hadamard gates. For the size and direction of rotation angle of quantum rotation gates, a simple determining method is proposed. Above operations extend the search of the solution space effectively. To predict sunspot as an example to validate the presented algorithm.
This paper proposes an image super-resolution restoration algorithm based on example learning and iterative directional kernel regression,which is used to solve the problem that the existing super-resolution restorati...
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This paper proposes an image super-resolution restoration algorithm based on example learning and iterative directional kernel regression,which is used to solve the problem that the existing super-resolution restoration algorithm based on example learning cannot effect the restored image has a small degree of matching with the sample library and the problem of image restoration in the presence of *** them,the example learning can achieve basic image *** the directional kernel regression,the estimated smoothing matrix can obtain the minimum mean square estimation after multiple iterations,and further optimize the super-resolution restored *** results show that the improved algorithm improves the robustness and edge preservation characteristics of the super-resolution restoration *** with the classical algorithm,the algorithm has better visual effects and the root mean square error can be improved over 15%,and reflect the effectiveness of the algorithm.
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