backpropagation neural net
Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network
... Abstract. Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use ...
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A subjective job scheduler based on a backpropagation neural network
... the neural network is an elementary information-processing ...artificial neural network, first it is to be decided how many neurons are to be used and how the neurons are to be connected to form a ...
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Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
... Abstract — The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we ...
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Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors
... minimise backpropagation errors in training a backpropagation neural network (BPNN) to predict the records related to the Chi- Chi earthquake from four seismic stations: Station-TAP003, ...
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Forecasting Currency Exchange Rates via Feedforward Backpropagation Neural Network
... Feedforward Backpropagation Neural Network (FBNN) model and its application to currency exchange rate ...in neural networks were optimized using gradient descent and backpropagation ...
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Rainfall Forecasting Using Backpropagation Neural Network
... of Neural Networks are Multilayer Perception (MLP) that being combined with Backpropagation ...propagation Neural Network resulted 94,36% accuracy, while research by Vamsidhar et ...that ...
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Backpropagation Neural Network Algorithm for Water Level Prediction
... Wireless Sensor Network (WSN) became one of the tools that able to communicate with the computer without going through the media cable. WSN ultrasonic sensor systems have been developed for water level measurements by ...
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Backpropagation Neural Network Experiment on Human Face Recognition
... For localization and normalization, the local view method for eigenface (use of a machine learning) approaches to deal with scaling variation. The image processing and computer vision have investigated the number of ...
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Development of a Genetic based Neural Network System for Online Character Recognition
... A system for online character recognition which reduced the recognition failure was developed using hybrid of structural and statistical features used for extracting features from character images. MGA was used for ...
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Radial basis function neural network learning with modified backpropagation algorithm
... Artificial Neural Network (ANN) was developed as a parallel distributed system that is inspired by the biological learning process of the human ...(SOM), Backpropagation Neural Network (BPNN), ...
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Prediction of Seismic zone in India using Neural Network Algorithms
... the Backpropagation neural network model for earthquake ...concludes backpropagation artificial neural network is a best suit for the non-linear relationship ...with Backpropagation ...
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Machine learning in Dynamic Adaptive Streaming over HTTP (DASH)
... Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing ...the backpropagation algorithm (BA), the ...
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Temperature-Based Feed-Forward Backpropagation Artificial Neural Network For Estimating Reference Crop Evapotranspiration In The Upper West Region
... y , is ETo estimated by the ANN models, and N is number of observations. The use of the LM algorithm in network training is relatively new [24]. Coulibaly et al. [25] reported that about 90% of the applications of ANNs ...
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Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three Dimensional Hydrodynamic Models
... A neural network consists of a large number of simple processing elements that are called neurons and ...the net to solve a ...2. Neural networks can be classified into many types based on their ...
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An Introduction to Pattern Recognition Michael Alder pdf
... This is not essentially different from measuring intersections with scan lines, except that the mask holes don't have to be lines, they can be any shape. Nor is it very different in principle from Exercise 1.6.3, where ...
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Application of Artificial Neural Networks for Analysis of Flexible Pavements under Static Loading of Standard Axle
... The performance of a neural network model mainly depends on the network architecture and parameter settings. One of the most difficult tasks in ANN studies is to find this optimal network architecture which is ...
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Study on Experiments of Artificial Neural Network Using Spatial Data
... The results proved that the ANN can capture complex relationship where the data is a combination of data identified spectral and non-spectral (Pradhan, 2007; Pradhan, 2010). In this study the accuracy of the simulation ...
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Model of Electric Power Load by Adaptive Neural Network
... from neural network is calculated, an error representing the difference between target output and calculated output from the system is ...of neural network is to modify the network, the weights, to minimize ...
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Research status and applications of nature-inspired algorithms for agri-food production
... The success of DL is built on a great amount of data and the state-of-the-art supercomputing power allows training of scalable large neural networks for better performance with more data than shallow ANNs in ML ...
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Implementation of Backpropagation Algorithm: A Neural Net- work Approach for Pattern Recognition
... A neural network model is a powerful tool used for various real life applications like time series predication, sequence detection, data filtering, pattern recognition and other intelligent tasks as performed by ...
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