By Robert J. Jannarone
Many tracking, forecasting, and keep an eye on operations happen in settings the place relationships between key measurements needs to be realized speedy. Examples are online business techniques the place influent fabric isn't constant over the years, power load or expense forecasting the place call for features swap rapidly,and wellbeing and fitness administration the place relationships between monitored variables has to be discovered for every patient-treatment mix. the answer awarded is a brand new neuro-computing procedure that learns in real-time, even if information arrival premiums are numerous million measurements in line with moment. The e-book describes advantages and contours of the method, statistical foundations for the approach, and a number of other similar types.
The publication additionally describes on hand method software.
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Additional info for Concurrent Learning and Information Processing: A Neuro-Computing System that Learns During Monitoring, Forecasting, and Control
Indeed, rapid learning methods may be viewed as nothing more than statistical regression methods with completely automated sample and estimation updating (se chapter 5). In terms of stock market forecasting and related applications, such as airline and hotel reservation forecasting, the statistical approach is widely used for daily updates. Hourly updates may also be possible with considerable preprogramming and intense statistician effort. However, rapid learning methods offer the only alternative for much higher learning rates.
Many control applications use PID controllers to maintain settings such as tank levels. PID controllers send control signals that depend on a recent history of previously recorded input signals. Each PID control signal is a weighted sum of three terms: one involving the current signal level, one involving the difference between two recent levels, and one involving the sum of previous values. Ordinarily, PID controllers are preprogrammed by setting the coefficients for the three terms. PID preprogramming may produce effective control systems over long periods, provided that plant conditions do not change significantly.
These planned improvements and case studies will be described later in this book and in future publications. Rapid Learning Applications 27 Summary This chapter uses a range of examples to demonstrate key benefits of rapid learning methods. The key feature of the rapid learning system is its capacity to learn during information processing. The main resulting benefit is its capacity to perform monitoring, forecasting, and control with optimal precision and without interruption, even when new relationships among measurements are developing quickly.