# back-propagation networkの例文

## 例文携帯版

- Application of improved back - propagation network in the evaluation of railway rock slope
- A particularly interesting item is the back - propagation network , a c package that illustrates a net that analyzes sunspot data
- Algorithms for defect classification are developed . classifiers are constructed based on back - propagation network . the network configuration bases on input and output
- The design theory of neural networks is discussed , including the basis principles of neuron control and the design of back - propagation network . 4
- The back - propagation network is the core part of ahead - propagation network in artificial neural network and is widely applied in many aspects such as function approach , mode distinguishing and data condensation
- Thickness is an effective feature for identifying corn from monocotyledonous weed , and the correctness was 90 % . ( 6 ) six shape features were used to design back - propagation network for weed identification and the network structure was 6 - 12 - 3
- Besides stability , bifurcation and chaos in neural networks have receiving much attention recently . in this dissertation , we propose two neuron models with chaotic dynamics , which constitute chaotic neural networks that encompassed various associative and back - propagation networks
- ( 2 ) combining secondary genetic algorithm with back - propagation network , the thesis redacts genetic neural network procedure , which optimizes number of hidden node and weight value and threshold value simultaneously . the procedure overcomes blindness during search , avoids falling into localminimum and increases learning accuracy
- Because wavelet transform has forceful ability to pick - up character and artificial neural network has a strong capability to classify information . in this paper , the wavelet network has been formed by the wavelet transform , which is multi - dimension wavelet , and the artificial neural network which is back - propagation network . taking the eigenvector as an input of the wavelet network , the wavelet network can fulfill diagnosis of faults
- This thesis expounds fundamental principle and realization technique of artificial neural network and genetic algorithm , and redacts artificial neural network procedures . - ( l ) adopting batch processing high - speed algorithm , the thesis redacts back - propagation network procedure to enchance training velocity , in which learning rate and momentum parameters are modulated self - adaptably during error correction
- In the final part of the paper , the feasibility of applying neural networks to evaluate the performance of the columns is investigated . a three - layer back - propagation network is trained using the earthquake - resistant behavior experimental data of the columns to predict the ductility of the columns . the predicted results agree well with the test results
- Artificial neural network can be used to detect oil & gas information from seismic data . self - organizing feature map and back - propagation network are discussed firstly . aimed at issue of local optimization in back - propagation network and based on chaotic feature in logistic equation , a kind of chaos optimized artificial neural network is presented
- ( 4 ) the application of artificial neural network in the field was studied . by bp ( back - propagation networks ) neural network and rational choice of the calculation factor , the relation between the improved mixture composition and combustion rate , and that between the charge of ignition rocket and p - t curve were simulated . the model could exhibit in essence the inherent relation and the forecasted results were in good agreement with the actual testing results , which showed that the model could be used as a guide for the design of the composition and the ignition engine , and that artificial neural network could be employed in the field for the purpose of reducing experimental work of hazardous materials
- Neural network method is applied to the strength prediction . the ratio of water to cement material , the mass of fly ash and the silicon fume are regarded as network inputs and th e 28d strength is the target . the inputs and target are used to train a three layers back - propagation network
- In order to overcome problems arisen from the application of x fluorescence analysis into complex spectrum produced by archaeological ceramic fragments with multi - element , low content and thick ground , we have employed the artificial neural network into the research of x fluorescence archaeology and conducted three kinds of research works . as the first one , we have applied the linear olam network ( optimal linear association memory network ) and the non - linear bp network ( back - propagation network ) respectively to analyze the complex x fluorescence spectrum of archaeological samples , and taken both results of spectrum analysis to compare with each other . the second , the method of pattern recognition of bp network was tentatively used to perform intelligent identification of production places of these archaeological samples
- This paper has introduced essential concept of artificial neural network , summarized basal theory of multilayer back - propagation network , put stress on expounding some issue on multilayer back - propagation network in practice , demonstrated the feasibility of resolving pile engineer with artificial neural network , discussed primary problems about some method and research of characteristic analysis on pile engineer these days , and analyzed data on test of single pile