| Techniques for Building Neural Networks: Introduction. What are Neural Networks? How Does Neural Computing Differ from Traditional Programming? How are Neural Networks Built? How do Neural Networks Learn? What Do I Need to Build an MLP? The Neural Project Life Cycle. The Generalisation-Accuracy Trade-Off. Implementation Details. Activation and Learning Equations. A Simple Example: Modelling a Pendulum. Data Encoding and Re-Coding: Introduction. Data Type Classification. Initial Statistical Calculations. Dimensionality Reduction. Scaling a Data Set. Neural Encoding Methods. Temporal Data. When To Carry Out Re-Coding. Implementation Details. Building a Network: Introduction. Designing the MLP. Training Neural Networks. Implementation Details. Time Varying Systems: Time Varying Data Sets. Neural Networks for Predicting or Classifying Time Series. Choosing the Best Method for the Task. Predicting More Than One Step Into the Future. Learning Separate Paths Through State Space. Recurrent Networks as Models of Finite State Automata. Summary of Temporal Neural Networks. Data Collection and Validation: Data Collection. Building the Training and Test Sets. Data Quality. Calculating Entropy Values for a DataSet. Using a Foward#&150 | |
| Inverse Model to Serve Ill Posed Problems. Output and Error Analysis: Introduction. What do the Errors Mean? Error Bars and Confidence Limits. Methods for Visualising Errors. Novelty Detection. Implementation Details. A Simple Two Class Example. Unbalanced Data: A Mail Shot Targeting Example. Auto-Associative Network Novelty Detection. Training a Network on Confidence Limits. An Example Based on Credit Rating. Network Use and Analysis: Introduction. Extracting Reasons. Traversing a Network. Summary. Calculating the Derivatives. Personnel Selection: A Worked Example. Managing a Neural Network Based Project: Project Context. Development Platform. Project Personnel. Project Costs. The Benefits of Neural Computing.The Risks Involved with Neural Computing. Alternatives to a Neural Computing Approach. Project Time Scale. Project Documentation. System Maintenance. Review of Neural Applications: Introduction to Part II. Neural Networks and Signal Processing: Introduction. Signal Processing as Data Preparation. Pre-Processing Techniques for Visual Processing. Neural Filters in the Fourier and Temporal Domains. Speech Recognition. Production Quality Control. An Artistic Style Classifier. Fingerprint Analysis.Summary. Financial and Business Modelling: Introduction. Market Modelling Financial Time Series Prediction. Review of Published Findings. Conclusion.Industrial Process Modelling: Introduction. Modelling and Controlling Dynamic Systems. Case Study: Predicting Driver Alertness. Training the Neural Networks. Robot Control by Reinforcement Learning. Summary. Conclusions: Summary. A Few Typical Mistakes Worth Remembering. Using the Accompanying Software: Introduction. Neural Network Code. Data Preparation Routines. Glossary. Bibliography. Index. |
