Sensitivity Analysis For Neural Networks
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Author |
: Daniel S. Yeung |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 89 |
Release |
: 2009-11-09 |
ISBN-10 |
: 9783642025327 |
ISBN-13 |
: 3642025323 |
Rating |
: 4/5 (27 Downloads) |
Synopsis Sensitivity Analysis for Neural Networks by : Daniel S. Yeung
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Author |
: Andries P. Engelbrecht |
Publisher |
: |
Total Pages |
: 255 |
Release |
: 1999 |
ISBN-10 |
: OCLC:668125497 |
ISBN-13 |
: |
Rating |
: 4/5 (97 Downloads) |
Synopsis Sensitivity Analysis of Multilayer Neural Networks by : Andries P. Engelbrecht
Author |
: Bryan Dong Koo Chung |
Publisher |
: |
Total Pages |
: 226 |
Release |
: 1993 |
ISBN-10 |
: OCLC:32827001 |
ISBN-13 |
: |
Rating |
: 4/5 (01 Downloads) |
Synopsis Sensitivity Analysis of Feedforward Neural Network Classifiers by : Bryan Dong Koo Chung
Author |
: Maosen Cao |
Publisher |
: |
Total Pages |
: |
Release |
: 2016 |
ISBN-10 |
: OCLC:1154230228 |
ISBN-13 |
: |
Rating |
: 4/5 (28 Downloads) |
Synopsis Advanced Methods in Neural Networks-Based Sensitivity Analysis with Their Applications in Civil Engineering by : Maosen Cao
Artificial neural networks (ANNs) are powerful tools that are used in various engineering fields. Their characteristics enable them to solve prediction, regression, and classification problems. Nevertheless, the ANN is usually thought of as a black box, in which it is difficult to determine the effect of each explicative variable (input) on the dependent variables (outputs) in any problem. To investigate such effects, sensitivity analysis is usually applied on the optimal pre-trained ANN. Existing sensitivity analysis techniques suffer from drawbacks. Their basis on a single optimal pre-trained ANN model produces instability in parameter sensitivity analysis because of the uncertainty in neural network modeling. To overcome this deficiency, two successful sensitivity analysis paradigms, the neural network committee (NNC)-based sensitivity analysis and the neural network ensemble (NNE)-based parameter sensitivity analysis, are illustrated in this chapter. An NNC is applied in a case study of geotechnical engineering involving strata movement. An NNE is implemented for sensitivity analysis of two classic problems in civil engineering: (i) the fracture failure of notched concrete beams and (ii) the lateral deformation of deep-foundation pits. Results demonstrate good ability to analyze the sensitivity of the most influential parameters, illustrating the underlying mechanisms of such engineering systems.
Author |
: Masato Koda |
Publisher |
: |
Total Pages |
: |
Release |
: 1994 |
ISBN-10 |
: OCLC:60254412 |
ISBN-13 |
: |
Rating |
: 4/5 (12 Downloads) |
Synopsis Neural Network Learning Based on Stochastic Sensitivity Analysis by : Masato Koda
Author |
: Andrea Saltelli |
Publisher |
: John Wiley & Sons |
Total Pages |
: 232 |
Release |
: 2004-07-16 |
ISBN-10 |
: 9780470870945 |
ISBN-13 |
: 047087094X |
Rating |
: 4/5 (45 Downloads) |
Synopsis Sensitivity Analysis in Practice by : Andrea Saltelli
Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.
Author |
: Frédéric Magoules |
Publisher |
: John Wiley & Sons |
Total Pages |
: 186 |
Release |
: 2016-02-08 |
ISBN-10 |
: 9781848214224 |
ISBN-13 |
: 1848214227 |
Rating |
: 4/5 (24 Downloads) |
Synopsis Data Mining and Machine Learning in Building Energy Analysis by : Frédéric Magoules
The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.
Author |
: Quan Tang |
Publisher |
: |
Total Pages |
: 196 |
Release |
: 2002 |
ISBN-10 |
: OCLC:50595313 |
ISBN-13 |
: |
Rating |
: 4/5 (13 Downloads) |
Synopsis Application of Artificial Neural Networks to Sensitivity Analysis and Modeling of Small Office Buildings by : Quan Tang
Author |
: Joao Luis Garcia Rosa |
Publisher |
: BoD – Books on Demand |
Total Pages |
: 416 |
Release |
: 2016-10-19 |
ISBN-10 |
: 9789535127048 |
ISBN-13 |
: 9535127047 |
Rating |
: 4/5 (48 Downloads) |
Synopsis Artificial Neural Networks by : Joao Luis Garcia Rosa
The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.
Author |
: Halil Ibrahim San |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2022 |
ISBN-10 |
: OCLC:1345544932 |
ISBN-13 |
: |
Rating |
: 4/5 (32 Downloads) |
Synopsis Global Sensitivity Analysis TRACE Model Data with Deep Neural Network Based Surrogate Models by : Halil Ibrahim San