LEADER 05010nam 2200685Ia 450 001 9910824289303321 005 20240404153156.0 010 $a1-281-86600-8 010 $a9786611866006 010 $a1-84816-146-8 035 $a(CKB)1000000000537749 035 $a(EBL)1681506 035 $a(OCoLC)748530828 035 $a(SSID)ssj0000104565 035 $a(PQKBManifestationID)11121879 035 $a(PQKBTitleCode)TC0000104565 035 $a(PQKBWorkID)10080105 035 $a(PQKB)11767443 035 $a(MiAaPQ)EBC1681506 035 $a(WSP)0000P225 035 $a(Au-PeEL)EBL1681506 035 $a(CaPaEBR)ebr10255373 035 $a(CaONFJC)MIL186600 035 $a(EXLCZ)991000000000537749 100 $a20010227d2001 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplication of neural networks and other learning technologies in process engineering /$feditors, I.M. Mujtaba, M.A. Hussain 205 $a1st ed. 210 $aRiver Edge, NJ $cICP$d2001 215 $a1 online resource (423 p.) 300 $aDescription based upon print version of record. 311 $a1-86094-263-6 320 $aIncludes bibliographical references. 327 $aContents ; Foreword ; Acknowledgements ; Part I: Modelling and Identification ; 1. Simulation of Liquid-Liquid Extraction Data with Artificial Neural Networks ; 2. RBFN Identification of an Industrial Polymerization Reactor Model ; 3. Process Identification with Self-Organizing Networks 327 $a4. Training Radial Basis Function Networks for Process Identification with an Emphasis on the Bayesian Evidence Approach 5. Process Identification of a Fed-Batch Penicillin Production Process - Training with the Extended Kalman Filter ; Part II: Hybrid Schemes 327 $a6. Combining Neural Networks and First Principle Models for Bioprocess Modeling 7. Neural Networks in a Hybrid Scheme for Optimisation of Dynamic Processes: Application to Batch Distillation ; 8. Hierarchical Neural Fuzzy Models as a Tool for Process Identification: A Bioprocess Application 327 $aPart III: Estimation and Control 9. Adaptive Inverse Model Control of a Continuous Fermentation Process Using Neural Networks ; 10. Set Point Tracking in Batch Reactors: Use of PID and Generic Model Control with Neural Network Techniques 327 $a11. Inferential Estimation and Optimal Control of a Batch Polymerisation Reactor Using Stacked Neural Networks Part IV: New Learning Technologies ; 12. Reinforcement Learning in Batch Processes ; 13. Knowledge Discovery through Mining Process Operational Data 327 $aPart V: Experimental and Industrial Applications 330 $a This book is a follow-up to the IChemE symposium on "Neural Networks and Other Learning Technologies", held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems - modelling, estimation, control, optimisation and 606 $aNeural networks (Computer science) 606 $aProcess control 606 $aManufacturing processes 615 0$aNeural networks (Computer science) 615 0$aProcess control. 615 0$aManufacturing processes. 676 $a006.3/2 701 $aMujtaba$b I. M$01604859 701 $aHussain$b M. A$g(Mohamed Azlan),$f1958-$01604860 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824289303321 996 $aApplication of neural networks and other learning technologies in process engineering$93929841 997 $aUNINA