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The BOXES methodology second editionblack box control of ill-defined systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The BOXES methodology second editionby David W. Russell.
其他題名:	black box control of ill-defined systems /
作者:	Russell, David W.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxii, 277 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Automatic control.
電子資源:	https://doi.org/10.1007/978-3-030-86069-1
ISBN:	9783030860691$q(electronic bk.)

The BOXES methodology second editionblack box control of ill-defined systems /
Russell, David W.

The BOXES methodology second editionblack box control of ill-defined systems /[electronic resource] :by David W. Russell. - Second edition. - Cham :Springer International Publishing :2022. - xxii, 277 p. :ill. (some col.), digital ;24 cm.

Introduction -- Part I: Learning and Artificial Intelligence (AI) -- The Game Metaphor -- Introduction to BOXES -- Dynamic control as a game -- Part II: The Trolley and Pole -- Control of a simulated inverted pendulum using the BOXES method -- The Liverpool experiment -- Solving the auto-start dilemma -- Part III: Other BOXES applications -- Continuous system control -- Other on/off control case studies -- Two non-linear applications -- Part IV: Improving the Algorithm -- Accelerated learning -- Two advising paradigms -- Evolutionary studies research -- Part V: Further Thoughts -- Detecting and handling jitter -- Fully trained cells -- Solving the system aging paradox -- Part VI: Conclusion.

This book focuses on how the BOXES Methodology, which is based on the work of Donald Michie, is applied to ill-defined real-time control systems with minimal a priori knowledge of the system. The method is applied to a variety of systems including the familiar pole and cart. This second edition includes a new section that covers some further observations and thoughts, problems, and evolutionary extensions that the reader will find useful in their own implementation of the method. This second edition includes a new section on how to handle jittering about a system boundary which in turn causes replicated run times to become part of the learning mechanism. It also addresses the aging of data values using a forgetfulness factor that causes wrong values of merit to be calculated. Another question that is addressed is "Should a BOXES cell ever be considered fully trained and, if so, excluded from further dynamic updates". Finally, it expands on how system boundaries may be shifted using data from many runs using an evolutionary paradigm.

ISBN: 9783030860691$q(electronic bk.)

Standard No.: 10.1007/978-3-030-86069-1doiSubjects--Topical Terms:

182078
Automatic control.

LC Class. No.: TJ213 / .R87 2022

Dewey Class. No.: 629.89

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505 0 $a Introduction -- Part I: Learning and Artificial Intelligence (AI) -- The Game Metaphor -- Introduction to BOXES -- Dynamic control as a game -- Part II: The Trolley and Pole -- Control of a simulated inverted pendulum using the BOXES method -- The Liverpool experiment -- Solving the auto-start dilemma -- Part III: Other BOXES applications -- Continuous system control -- Other on/off control case studies -- Two non-linear applications -- Part IV: Improving the Algorithm -- Accelerated learning -- Two advising paradigms -- Evolutionary studies research -- Part V: Further Thoughts -- Detecting and handling jitter -- Fully trained cells -- Solving the system aging paradox -- Part VI: Conclusion.
520 $a This book focuses on how the BOXES Methodology, which is based on the work of Donald Michie, is applied to ill-defined real-time control systems with minimal a priori knowledge of the system. The method is applied to a variety of systems including the familiar pole and cart. This second edition includes a new section that covers some further observations and thoughts, problems, and evolutionary extensions that the reader will find useful in their own implementation of the method. This second edition includes a new section on how to handle jittering about a system boundary which in turn causes replicated run times to become part of the learning mechanism. It also addresses the aging of data values using a forgetfulness factor that causes wrong values of merit to be calculated. Another question that is addressed is "Should a BOXES cell ever be considered fully trained and, if so, excluded from further dynamic updates". Finally, it expands on how system boundaries may be shifted using data from many runs using an evolutionary paradigm.
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