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Exploratory analysis of met...
This volume is concerned with the analysis and interpretation of multivariate measurements commonly found in the mineral and metallurgical industries, with the emphasis on the use of neural networks. The book is primarily aimed at the practicing metallurgist or process engineer, and a considerable part of it is of necessity devoted to the basic theory which is introduced as briefly as possible within the large scope of the field. Also, although the book focuses on neural networks, they cannot be divorced from their statistical framework and this is discussed in length. The book is there
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monografia Rebiun21659587 https://catalogo.rebiun.org/rebiun/record/Rebiun21659587 m o d | cr -n--------- 020107s2002 ne a ob 001 0 eng 2002016356 1-281-01900-3 9786611019006 0-08-053146-6 UPVA 996969826103706 UCAR 991008502965204213 MiAaPQ MiAaPQ MiAaPQ eng 669.0285 669/.07/2 21 Aldrich, C. Chris) Exploratory analysis of metallurgical process data with neural networks and related methods electronic resource] C. Aldrich 1st ed Amsterdam Boston Elsevier 2002 Amsterdam Boston Amsterdam Boston Elsevier 1 online resource (387 p.) 1 online resource (387 p.) Text txt computer c online resource cr Process metallurgy 12 Description based upon print version of record Includes bibliographical references (p. 333-365) and index Front Cover; Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods; Copyright Page; Preface; Table of Contents; CHAPTER 1. INTRODUCTION TO NEURAL NETWORKS; 1.1. BACKGROUND; 1.2. ARTIFICIAL NEURAL NETWORKS FROM AN ENGINEERING PERSPECTIVE; 1.3. BRIEF HISTORY OF NEURAL NETWORKS; 1.4. STRUCTURES OF NEURAL NETWORKS; 1.5. TRAINING RULES; 1.6. NEURAL NETWORK MODELS; 1.7. NEURAL NETWORKS AND STATISTICAL MODELS; 1.8. APPLICATIONS IN THE PROCESS INDUSTRIES; CHAPTER 2. TRAINING OF NEURAL NETWORKS; 2.1. GRADIENT DESCENT METHODS; 2.2. CONJUGATE GRADIENTS 2.3. NEWTON'S METHOD AND QUASI-NEWTON METHOD2.4. LEVENBERG-MARQUARDT ALGORITHM; 2.5. STOCHASTIC METHODS; 2.6 REGULARIZATION AND PRUNING OF NEURAL NETWORK MODELS; 2.7 PRUNING ALGORITHMS FOR NEURAL NETWORKS; 2.8. CONSTRUCTIVE ALGORITHMS FOR NEURAL NETWORKS; CHAPTER 3. LATENT VARIABLE METHODS; 3.1. BASICS OF LATENT STRUCTURE ANALYSIS; 3.2. PRINCIPAL COMPONENT ANALYSIS; 3.3. NONLINEAR APPROACHES TO LATENT VARIABLE EXTRACTION; 3.4. PRINCIPAL COMPONENT ANALYSIS WITH NEURAL NETWORKS 3.5. EXAMPLE 2: FEATURE EXTRACTION FROM DIGITISED IMAGES OF INDUSTRIAL FLOTATION FROTHS WITH AUTOASSOCIATIVE NEURAL NETWORKS3.6. ALTERNATIVE APPROACHES TO NONLINEAR PRINCIPAL COMPONENT ANALYSIS; 3.7. EXAMPLE 1: LOW-DIMENSIONAL RECONSTRUCTION OF DATA WITH NONLINEAR PRINCIPAL COMPONENT METHODS; 3.8. PARTIAL LEAST SQUARES (PLS) MODELS; 3.9. MULTIVARIATE STATISTICAL PROCESS CONTROL; CHAPTER 4. REGRESSION MODELS; 4.1. THEORETICAL BACKGROUND TO MODEL DEVELOPMENT; 4.2. REGRESSION AND CORRELATION; 4.3. MULTICOLLINEARITY; 4.4. OUTLIERS AND INFLUENTIAL OBSERVATIONS; 4.5. ROBUST REGRESSION MODELS 4.6. DUMMY VARIABLE REGRESSION4.7. RIDGE REGRESSION; 4.8. CONTINUUM REGRESSION; 4.9. CASE STUDY: CALIBRATION OF AN ON-LINE DIAGNOSTIC MONITORING SYSTEM FOR COMMINUTION IN A LABORATORY-SCALE BALL MILL; 4.10. NONLINEAR REGRESSION MODELS; 4.11. CASE STUDY 1: MODELLING OF A SIMPLE BIMODAL FUNCTION; 4.12. NONLINEAR MODELLING OF CONSUMPTION OF AN ADDITIVE IN A GOLD LEACH PLANT; CHAPTER 5. TOPOGRAPHICAL MAPPINGS WITH NEURAL NETWORKS; 5.1. BACKGROUND; 5.2. OBJECTIVE FUNCTIONS FOR TOPOGRAPHIC MAPS; 5.3. MULTIDIMENSIONAL SCALING; 5.4. SAMMON PROJECTIONS 5.5. EXAMPLE 1: ARTIFICIALLY GENERATED AND BENCHMARK DATA SETS5.6. EXAMPLE 6.5. SIMPLE EXAMPLES OF HIERARCHICAL AND K-MEANS CLUSTER ANALYSIS This volume is concerned with the analysis and interpretation of multivariate measurements commonly found in the mineral and metallurgical industries, with the emphasis on the use of neural networks. The book is primarily aimed at the practicing metallurgist or process engineer, and a considerable part of it is of necessity devoted to the basic theory which is introduced as briefly as possible within the large scope of the field. Also, although the book focuses on neural networks, they cannot be divorced from their statistical framework and this is discussed in length. The book is there English Metallurgy Metallurgical research Electronic books Aldrich, C. Chris) 0-444-50312-9 Process metallurgy 12