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OPTIMIZATION OF EFFLUENTS USING A NEURAL NETWORK IN THE TREATMENT OF INDUSTRIAL WASTEWATER
The growth of the planet's population leads to an increase in the problem of access to fresh water. The main sources of water on Earth are brackish and sea water. In connection with the water crisis, ...
View Book →A constructive learning algorithm based on back-propagation
First published in 1995...
View Book →1991 IEEE International Conference on Systems, Man, and Cybernetics
No description available....
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Classification of underwater signals using a back-propagation neural network
First published in 1997...
View Book →Simulation - Past, Present and Future
No description available....
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Memory-based control with recurrent neural networks
Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control -- deterministic policy gradient and stochas...
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An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and De
Coffee Leaf Rust (CLR), Coffee Berry Disease (CBD) and Coffee Wilt Disease (CWD) are the three main diseases that attack coffee plants. This paper presents the identification of these types diseases u...
View Book →Neural Networks and Fuzzy Systems
Written by one of the foremost experts in the field of neural networks, this is the first book to combine the theories and applications or neural networks and fuzzy systems. The book is divided into t...
View Book →Backpropagation network for gesture recognition
First published in 1996...
View Book →New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
First published in 2016...
View Book →IEEE ... International Conference on Neural Networks
No description available....
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End-to-End Learning for Image Burst Deblurring
We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network ...
View Book →A study of the generalizability of a backpropagation network
First published in 1990...
View Book →Artificial Neural Networks
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learnin...
View Book →The Roots of Backpropagation
Now, for the first time, publication of the landmark work inbackpropagation! Scientists, engineers, statisticians, operationsresearchers, and other investigators involved in neural networkshave long s...
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Study of resonant microstrip antennas on artificial neural networks
This paper presents a new model based on the backpropagation multilayered perception network to find accurately the bandwidth of both electrically thin and thick rectangular microstrip antennas. This ...
View Book →Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks
World-renowned contributors present papers concerning algorithms used on the latest generation of parallel machines (MIMD). Details key applications running the gamut from medical imaging, visualizati...
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Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network
The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) w...
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NASA Technical Reports Server (NTRS) 19980025539: Neural Network Prediction of Aluminum-Lithium Weld
Acoustic Emission (AE) flaw growth activity was monitored in aluminum-lithium weld specimens from the onset tensile loading to failure. Data on actual ultimate strengths together with AE data from the...
View Book →The Handbook of Brain Theory and Neural Networks
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? ...
View Book →Using Neural Networks for Risk Assessment in Internal Auditing
No description available....
View Book →Parallel Implementations of Backpropagation Neural Networks on Transputers
This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism....
View Book →Optics in Atmospheric Propagation and Adaptive Systems
No description available....
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DTIC ADA247003: An Evolutionary Approach to Designing Neural Networks
One of the most interesting properties of neural networks is their ability to learn appropriate behavior by being trained on examples. Established learning algorithms which typically work by minimizin...
View Book →Learning nonlinear constraints with contrastive backpropagation
First published in 2004...
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Nonlinear adaptive control using backpropagating neural networks
First published in 1992...
View Book →Neural Network Control Of Robot Manipulators And Non-Linear Systems
There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically....
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DTIC ADA246997: Backpropagation Neural Network for Noise Cancellation Applied to the NUWES Test Rang
This thesis investigates the application of backpropagation neural networks as an alternative to adaptive filtering at the NUWES test ranges. To facilitate the investigation, a model of the test range...
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DTIC ADA246626: An Exploratory Application of Neural Networks to the Sortie Generation Forecasting P
This exploratory study assesses the accuracy of backpropagation neural networks in predicting sortie generations, given pre-specified levels of air base resources. Single hidden layer networks and two...
View Book →Environmental Protection and Sustainable Development
Selected, peer reviewed papers from the 2013 2nd International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2013), 28-29 December, 2013, Shenzhen, China...
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Coal-Fired Boiler Fault Prediction using Artificial Neural Networks
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigat...
View Book →Introduction to Neural Networks with Java
Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforwar...
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Reservoir computing for spatiotemporal signal classification without trained output weights
Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data. Rather than training the network node connection...
View Book →Computational Intelligence
The definitive survey of computational intelligence from luminaries in the field Computational intelligence is a fast-moving, multidisciplinary field - the nexus of diverse technical interest areas th...
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Ranking via Sinkhorn Propagation
It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact that the space of pe...
View Book →Science of Artificial Neural Networks
No description available....
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LVQ and backpropagation neural networks applied to NASA SSME data
First published in 1993...
View Book →Backpropagation
Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as ...
View Book →Thermal-wave slice diffraction tomography with backpropagation and transmission reconstructions
First published in 1996...
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Proceedings
Metrics and quality assurance; knowledge and logic based systems; object-orientated techniques; validation and verification; distributed and mobile systems; software design methodology; software proce...
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Fast Second-Order Stochastic Backpropagation for Variational Inference
We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed ...
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Physiological Maturation of Regenerating Hair Cells
The bullfrog saccule, a sensor of gravity and substrate-borne vibration, is a model system for hair cell transduction. Saccular hair cells also increase in number throughout adult life and rapidly rec...
View Book →Artificial Neural Networks, 2
No description available....
View Book →Temporal learning using time-dependent backpropagation and teacher forcing
First published in 1997...
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Image analysis for classifying coffee bean quality using a multi feature and machine learning approa
Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly unde...
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How Important is Weight Symmetry in Backpropagation?
Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport problem" (Grossberg 1987)...
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Sampling-based Gradient Regularization for Capturing Long-Term Dependencies in Recurrent Neural Netw
Vanishing (and exploding) gradients effect is a common problem for recurrent neural networks with nonlinear activation functions which use backpropagation method for calculation of derivatives. Deep f...
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Beyond Feedforward Models Trained by Backpropagation: a Practical Training Tool for a More Efficient
Cellular Simultaneous Recurrent Neural Network (SRN) has been shown to be a function approximator more powerful than the MLP. This means that the complexity of MLP would be prohibitively large for som...
View Book →Parallel Computing on Distributed Memory Multiprocessors
Proceedings of the NATO Advanced Study Institute on Parallel Computing on Distributed Memory Multiprocessors, held at Bilkent University, Ankara, Turkey, July 1-13, 1991...
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Multi-class chronic lung disease classification based on guided backpropagation convolutional neural
Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to...
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