Neural Networks: A Classroom Approach. Front Cover · Satish Kumar. Tata McGraw-Hill Education, - Neural networks (Computer science) - pages. Neural Networks: A Classroom Approach. Front Cover. Satish Kumar. Tata McGraw-Hill, - Neural networks (Computer science) - pages. 0 Reviews. Neural Networks is an integral component fo the ubiquitous soft computing paradigm. Neural Networks: A Classroom Approach, achieves a balanced blend of.

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This revised edition of Neural Networks is an up-to-date exposition of the subject and continues to provide an understanding of the underlying. - download Neural Networks - A Classroom Approach book online at best prices in India on Read Neural Networks - A Classroom Approach. Neural Networks is an integral component fo the ubiquitous soft computing paradigm. An in-depth understanding of this field requires some background of the.

Printed in the USA. All fightsreserved. Corresponding to these two approaches to prediction, there are two ways that neural network studies can contribute to control theory. First, in what we will call type I applications, neural network studies provide new ideas for implementing the classical approach to control through modeling. In other words, studies of this type identify neural subsystems e.

The result is that these higher centers can control joint compliances independently from joint positions without solving a difficult prediction problem.

On the other hand, in what we will call type II applications, neural network studies provide new methods for prediction by performing the equivalent of multivariate nonlinear curve fitting. Here, the back-propagation learning technique has been most widely used to date because of its well-known ability to synthesize a network that can mimic arbitrary nonlinear vector functions.

There is nothing problematic about type I applications of neural network studies to control theory. However, because control engineers trained in the classical method expect to understand why a given control strategy is effective for a given controlled system, they may be reluctant to use type II applications of neural networks.

Advocates of the latter often claim it as an advantage that a network-based controller can converge to a solution even though it knows nothing of the first principles governing the controlled system's behavior. However, from the classical perspective, it is an admission of defeat to say that "it works, in the sense that the error has been eliminated by learning, but we do not know why.

Such an argument rests on the supposition that the higher brain controls the body's effector systems without benefit of internal models with structures corresponding to those that might be built from first principles.

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Instead, the brain is supposed to rely on "brute force" associative learning of arbitrary nonlinear functions. The most direct treatment of these and related issues appears in the chapter by Andrew Barto, which the editors wisely placed first in the volume. Barto stakes out a middle ground between physical model classicists and advocates of type II applications.

Miller, R. Sutton, a n d P. Consistent with the varied backgrounds of the workshop attendees, this collection of works endeavors not only to present current theoretical research in neural network-based control systems, but also aims to identify industrial applications where neural networks may prove useful in the near future. In addition, eight benchmark control problems are included in an appendix.

The main theme pervading this effort concerns the synthesis of neural network methods with existing knowledge from the well-studied fields of conventional and adaptive control theory. Perhaps due to the focus on engineering control theory, little direct contact is made with issues involving control in biological systems. Control problems arise when we try to make a physical system exhibit a desired response.

If we know the functions relating inputs to outputs and vice versa for the system, we can use these functions to predict system behavior and control the system's response by judicious choice of inputs. Unfortunately, most complex systems exhibit highly nonlinear input-output functions. Moreover, because the complexity of such systems is often necessary for their intended function e.

Therefore, it becomes necessary to solve a difficult prediction problem as a preliminary to effective control. A classical approach to predicting the behavior of such systems is to "open up the black box" i. An accurate model allows the overall input-output function to be computed as a composition of more elementary functions. Moreover, to paraphrase the Swiss structuralist Piaget, "to understand is to reconstruct.

However, another path can be taken to predicting the response of a system exhibiting a highly nonlinear input-output function: perform a variety of nonlinear regression analysis, or least-squares curve fitting, on a sufficiently broad sample of input-output pairs.

Such an analysis can often yield suffi- it i s. To the extent that a class of models can represent any structure, it cannot be expected to produce meaningful extrapolations beyond the data on which it was trained. Nevertheless, he sees back propagation as on the same continuum as classical modeling: whereas classical modeling sits on the end of the continuum where representational restrictions are high, BP sits on the end of the continuum where such restrictions are low.

In addition to providing an excellent introduction to the field through his lucid treatment of these issues, Barto furnishes a useful framework for understanding the more specialized chapters which follow. The key component of this framework is a description of five supervised learning schemes which arise repeatedly in the following chapters.

Chapter 3 by Paul Werbos, now widely acknowledged as the inventor of back propagation methods in Werbos , primarily investigates the use of "adaptive critics" in reinforcement learning with emphasis on the relation between these critics and the control theory method of dynamic programming.

This chapter will be most rewarding after a review of standard dynamic programming algorithms since the author spends little time expositing basic ideas. Chapter 4 by Ronald Williams presents a well-written introduction to recurrent networks with primary emphasis on gradient descent methods for training these networks.

Williams outlines the back propagation-through-time and the real-time recurrent learning algorithms. In addition, Williams includes a philosophical discussion of possible approaches to using neural networks for control, taking care to tie these concepts to those introduced by Barto in chapter 1 as well as to concepts from traditional control theory. Williams particularly emphasizes what he terms a radical approach within which connectionist models are used to model systems whose state cannot be determined by a fixed set of finitely many past values of its input and output.

According to Williams, the radical approach has no natural counterpart in the realm of adaptive linear filters; recurrent networks, however, are well-suited to model such systems. This approach thus provides the largest potential for novel contribution by neural network methods.

In chapter 5, Kumpati Narendra directly addresses the major theme of this book; namely, he investigates the use of well-understood adaptive control techniques for studying neural network control schemes. Narendra describes in greater detail some of the supervised learning schemes introduced F. Guenther and D. Bullock by Barto, focusing on the use of back propagation networks as natural substitutes for the adaptive components already found within standard adaptive control systems.

In summary, Narendra makes an assertion consistent with the views of several other authors in this book: a well-developed theory of control using neural networks will require solution of many outstanding problems such as system stability, but current simulation results indicating the effectiveness of such systems on difficult control problems provides ample justification for continued study of these problems.

One of the few chapters actually comparing a neural network to standard adaptive alternatives is chapter 6 by Gordon Kraft III and David Campagna. The authors conclude that the CMAC compares favorably on three criteria: nonrestriction to linear systems, noise rejection, and implementation speed for real-time control. However, it compared unfavorably on convergence rate, presumably because the adaptive task for both other controllers was restricted to estimating the values of parameters in a pre-existing model.

David E Shanno provides oft-overlooked alternatives to the steepest descent methods typically used to train neural networks in chapter 7.

Included are Newton, quasi-Newton, and conjugate gradient methods for parameter estimation in large-scale optimization problems. Pointers are given to more complete treatments of these methods in the numerical algorithms literature. Although cheap and easy to implement, steepest descent methods can suffer from extremely slow convergence.

These alternatives can provide much faster convergence with varying computational and memory requirements. Convergence time surfaces as an important theme once again in chapter 8, the final chapter in the general principles section of the book.

Here Richard Sutton constructs a simple adaptive path planner to illustrate the large benefits that can accrue when what the early cognitive psychologist Tolman called "vicarious trial and error" is combined with a reinforcement learning rule suitable for temporal credit assignment.

Like the adaptive critic approach described by Werbos in chapter 3, this planner, called Dyna, is based on dynamic programming principles from control theory.

Sutton describes results of a study which shows that a system able to "perform" trial and error actions both in the world and in imagination with the aid of an internalized world model converges much more quickly to an optimal policy than a system restricted to performance in the world. Because this chapter is intended to present general neural network control principles, it is unfortunate that primary emphasis is on a particular planning model with too little discussion of possible implications and applications of the ideas inherent to this model for neural network control.

In chapter 9 Mitsuo Kawato initiates the section on motion planning with a survey that encompasses both inverse kinematics and inverse dynamics, but with an emphasis on the latter.

Kawato is well-known for his work on combining feedback controllers with adaptive feedforward controllers that are trained by the error signals arising within the feedback controller.

Though he has explored a wide range of network Book Review designs, an enduring aspect of his approach since at least is based on the hypothesis that in the brain the cerebellum is an error-trained feedforward controller.

An idea pursued in the work of Kawato and others e. Instead, one can simply input the desired kinematics to both a low-gain feedback controller and an initially low-gain feedforward controller, and use the error signals from the feedback controller to slowly increment gains through the sidepath-feedforward controller.

Eventually, the feedback controller is largely "unloaded" because the predictable component of its work load has been taken over by the feedforward controller, which has learned the inverse dynamics i. Designs for this kind of autonomous supersession of control allow a system to be robust in nonstationary environments. The robustness derives from the existence of a fallback mode e. In the current work, Kawato addresses the ill-posedness i. Different models for learning inverse dynamics are discussed with emphasis on their ability to cope with this ill-posedness.

In chapter 10, Bartlett Mel continues the theme of vicarious trial and error raised in Sutton's contribution. He explores a system in which the primary mapping learned from experience and applied during performance is a forward kinematics mapping from initial states and unit joint angle perturbations to implied motions of a robot arm. His robotic system uses this learned map to "mentally" search for an arm trajectory capable of bridging the gap between an initial and a desired endpoint position without having any part of the arm collide with obstacles.

This task is simplified relative to standard techniques by eschewing all explicit geometric computations and relying on iterative use of the learned map to generate a visual representation of the expected 2-D area displaced by the arm following a candidate vector of unit joint rotations.

This visual representation arises within the same visual representation field used to register the positions of obstacles, so an expected collision is specified by overlap of imagined arm and actual object. Mel argues that because explicit geometric modeling is so compute-intensive, replacement of classical geometric modeling by a neural map might yield a large performance gain.

Mel's method of sensory-based motion planning which trades optimality for ease of computation represents an approach to neural network control which is radically different from any other in the book. In chapter 11, Christopher Atkeson and David Reinkensmeyer discuss the use of associative content-addressable memories ACAMs in a simple control scheme which stores "experiences" in a memory, then uses a parallel search during performance to find the stored experience which best matches current needs.

Although simpler than the CMAC, this system sacrifices the CMAC property of automatic generalization arising from continuity and overlapping receptive fields. Nonetheless, the modestly-named "feasibility studies" summarized in this chapter show reasonable performance with one caveat: possibly due to the lack of generalization, the system often gets stuck on performance plateaus well before errors approach zero.

Although not implemented with neural networks in this chapter, Atkeson and Reinkensmeyer discuss possible neural network implementations of ACAMs. Other techniques. A visual proof that neural nets can compute any function Two caveats Universality with one input and one output Many input variables Extension beyond sigmoid neurons Fixing up the step functions Conclusion.

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Why are deep neural networks hard to train? The vanishing gradient problem What's causing the vanishing gradient problem? Unstable gradients in deep neural nets Unstable gradients in more complex networks Other obstacles to deep learning. Deep learning Introducing convolutional networks Convolutional neural networks in practice The code for our convolutional networks Recent progress in image recognition Other approaches to deep neural nets On the future of neural networks.

Is there a simple algorithm for intelligence? If you benefit from the book, please make a small donation. Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Thanks also to all the contributors to the Bugfinder Hall of Fame. Code repository. Michael Nielsen's project announcement mailing list. Neural Networks and Deep Learning is a free online book.

The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.

This book will teach you many of the core concepts behind neural networks and deep learning.Not really recommended. Fuzzy Logic with Engineering Applications, 3ed.

If you are serious about understanding all the nuances, both theoretical and applied start reading this book. Michael A.

Artificial Neural Network Modelling

In addition to the truck backerupper, bioreactor, and autolander problems outlined in chapters 12, 16, and 17, problems of pole balancing, ship steering, and manipulator dynamics are described, as well as two problems developed for the American Control Conference in Atlanta, GA.

Instead, the brain is supposed to rely on "brute force" associative learning of arbitrary nonlinear functions. Please try again later. Furthermore, emphasis on networks with a more-than-superficial relationship to biology is minimal. Printed in the USA.

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