Research on the Application of Micro-Program Controller in Parallel Neural Networks

2011 ◽  
Vol 271-273 ◽  
pp. 1023-1028
Author(s):  
Xi Huang ◽  
Ping Wang ◽  
Zong Huang Weng ◽  
Xiao Zhang ◽  
Wei Han Zhong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Parallel Computing ◽  
Bp Neural Network ◽  
Network Control ◽  
Control Circuit ◽  
Bp Network ◽  
Hardware Circuit ◽  
Program Controller ◽  
The Way

In this paper a pipelined array of neurons based on the micro-program controller is proposed as the BP network control circuit implementations, without changing the hardware circuit under the premise of the way by increasing the instruction to meet the BP neural network parallel computing applications to enhance the flexibility of hardware.

Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment

2016 ◽  
Vol 847 ◽  
pp. 440-444 ◽  
Author(s):  
Yu Hui Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Current Knowledge ◽  
Horizontal Displacement ◽  
Structural Deformation ◽  
Bp Network ◽  
Damage Location

BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

The Design of the Resistance-Heated Furnace Control Based on BP Network PID Algorithm

2011 ◽  
Vol 214 ◽  
pp. 281-285
Author(s):  
Huai Zhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Mathematic Model ◽  
Network Control ◽  
Control Effect ◽  
Bp Network ◽  
Pid Algorithm ◽  
Highly Nonlinear ◽  
Furnace Control ◽  
Approximation Capability

The resistance-heated furnace control system has bigger lagging nature, so it is difficult to build accurate mathematic model BP neural network has the capability of expression nonlinearity and also has the self study and adaptive function. BP neural network control makes full use of neural network approximation capability, and with better control in resolving the highly nonlinear seriously uncertain systems. Simulated result indicates this control is able to make system reach satisfied control effect, so it has fairly good application value.

Neural Networks Model of Structural Damage Detection with Flexibility Matrix

2017 ◽  
Vol 872 ◽  
pp. 383-390
Author(s):  
Lin Li ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Bp Network ◽  
Simply Supported ◽  
Flexibility Matrix ◽  
Modal Flexibility ◽  
Damage Extent

As structural health problems are becoming more and more important, a neural networks model is introduced to detect structural damage. The structural modal flexibility matrix can be accurately constructed by the natural frequency and modal information. All elements of changes in the modal flexibility matrix are looked on as inputs of the networks. Damage locations and extents are both considered with different outputs in the present study. A simply supported truss structure is studied with different damage cases. To localize damage, one case is chosen as location input/target pairs to train the present BP network model. But to identify damage extent, two cases are chosen as extent pairs to train. Although modals of BP neural networks with different outputs are presented for different damage detecting schemes, it is more difficult to ascertain damage extent than location. The results indicate that the present BP neural network modal can effectively detect damage of structures with changes in the flexibility matrix between the intact and the damaged cases.

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

scholarly journals The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension

2011 ◽  
Vol 1 ◽  
pp. 163-167
Author(s):  
Da Ke Wu ◽  
Chun Yan Xie
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Input Data ◽  
Bp Network ◽  
Neural Network Prediction ◽  
Infected Tomato ◽  
Network Prediction ◽  
Hidden Nodes

Leafminer is one of pest of many vegetables, and the damage may cover so much of the leaf that the plant is unable to function, and yields are noticeably decreased. In order to get the information of the pest in the vegetable before the damage was not serious, this research used a BP neural network to classify the leafminer-infected tomato leaves, and the fractal dimension of the leaves was the input data of the BP neural network. Prediction results showed that when the number of FD was 21 and the hidden nodes of BP neural network were 21, the detection performance of the model was good and the correlation coefficient (r) was 0.836. Thus, it is concluded that the FD is an available technique for the detection of disease level of leafminer on tomato leaves.

scholarly journals The Research of Temperature Compensation for Thermopile Sensor Based on Improved PSO-BP Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuanjiang Li ◽  
Yuehua Li ◽  
Feng Li ◽  
Bin Zhao ◽  
QingQing Li
Keyword(s):  
Neural Network ◽  
Thermal Radiation ◽  
Bp Neural Network ◽  
Temperature Compensation ◽  
Safety Monitoring ◽  
Bp Algorithm ◽  
Bp Network ◽  
The Core ◽  
Thermopile Sensor ◽  
Improved Pso

When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

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