I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Review of shape representation and description techniques

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Biomechanics And Motor Control Of Human Movement

Department of Mechanical Engineering, Faculty of Engineering SCience, Osaka University, Toyonaka, Osaka, 560 Japan A new approach to the dynamic control of manipulators is developed from the viewpoint of mechanics. It is /irst shown that a linear feedback of generalized coordinates and their derivatives are effective for motion control in the large. Next, we propose a method for task-oriented coordinate control which can be easily implemented by a micro-computer and is suited to sensor feedback control. The proposed method is applicable even when holonomic constraints are added to the system. Effectiveness of the proposed method is verified by computer simulation.

A new feedback method for dynamic control of manipulators

The general problem of control of state-space constrained dynamic systems is considered. Based on the derivation of the constraint forces as explicit functions of the state and the input, a general model is presented that encompasses both the constrained case and the corresponding unconstrained case. Stability and point-to-point motion of these systems in the vicinity of an operating point are considered under operating conditions which either maintain or deliberately violate the constraints. Algorithms for computation of the necessary feedback gains in the vicinity of the operating point are discussed. For a three-link biped model, several motions in the vicinity of the vertical stance are considered, and the necessary feedback gains are derived. Digital computer simulation of some biped motions are carried out to serve as examples and to demonstrate use of the theory. This work is to be regarded as an elementary step in better understanding of human motor control and in the design of robots and prosthetic devices.

Modeling and control of constrained dynamic systems with application to biped locomotion in the frontal plane

While biped locomotion involves very complicated dynamical processes, a good deal can be learned about stability and feedback control from an analysis of simplified mathematical models. This paper treats locomotion dynamics relative to planar motion under an assumption that leg mass can be ignored in comparison to body mass. Thus the hypothetical biped possesses one rotational degree of freedom and two translational degrees, leading to a sixth-order system of nonlinear differential equations. These equations are linearized and feedback control laws are then derived to produce the desired stable forward motion. The feedback laws proposed involve a combination of continuous and discrete concepts to produce both step length and step period control as well as control of body attitude and altitude. The applicability of the control laws to the nonlinear system in the presence of large disturbances is verified by computer simulation. Hopefully, the results presented are significant relative to control processes arising in lower extremity prostheses and orthoses as well as to the design of biped robots.

On the Dynamic Stability of Biped Locomotion

In this article the collision of a robot with its environment is studied. In normal applications of a robot arm, a collision takes place because of the velocity of the end effector relative to the object at the time of contract. The collision has effects on the velocities and internal forces of the robotic system. Firstly, the generalized velocities representing joint rates have abrupt changes at the moment of collison with the environment. The mathematical model is derived to establish the quantitative relationship between this abrupt change and the severity of the collision. The latter is represented by either an external impulsive force or the instantaneous change of the linear velocity of the contact point. Secondly, internal to the system, large impulsive forces and torques of constraint may develop at each joint because of the collision. These impulses cause possible damages to the system. The mathematical model is also derived to establish a quantitative relation between the impulsive forces and torquest of constraint and the collision. These two models are applied to a Stanford Arm designed to pick up an object by its end effector, and the consequences of the collision are analyzed.

Mathematical modeling of a robot collision with its environment

A set of van der Pol oscillators is arranged in a network in which each oscillator is coupled to each other oscillator. Through the selection of coupling coefficients, the network is made to appear as a ring and as a chain of coupled oscillators. Each oscillator is provided with amplitude, frequency, and offset parameters which have analytically indeterminable effects on the output waves. These systems are simulated on the digital computer in order to study the amplitude, frequency, offset, and phase relationships of the waves versus parameter changes. Based on the simulations, systems of coupled oscillators are configured so that they exhibit stable patterns of signals which can be used to model the central pattern generator (CPG) of living organisms. Using a simple biped as an example locomotory system, the CPG model generates control signals for simulated walking and jumping maneuvers. It is shown that with parameter adjustments, as guided by the simulations, the model can be made to generate kinematic trajectories which closely resemble those for the human walking gait. Further-more, minor tuning of these parameters along with some algebraic sign changes of coupling coefficients can effect a transition in the trajectories to those of a two-legged hopping gait. The generalized CPG model is shown to be versatile enough that it can also generate various n-legged gaits and spinal undulatory motions, as in the swimming motions of a fish.

Modeling of a Neural Pattern Generator with Coupled nonlinear Oscillators

The feasibility of a method for the identification of a three-dimensional object from information contained in the boundary of its silhouettes is demonstrated. A silhouette is characterized by parametric representation of its boundary curve in the complex plane. After normalization and transformation, a set of Fourier descriptors is derived for every silhouette. A minimum distance classifier uses the descriptors to identify the three-dimensional object and to estimate its position and attitude with respect to a known reference coordinate system. The method was tested for identification of four aircraft representing complex and nonconvex objects. Simulation results, quantitative and statistical, are presented.

Hooshang Hemami

Papers

Modeling and control of constrained dynamic systems with application to biped locomotion in the frontal plane

On the Dynamic Stability of Biped Locomotion

Mathematical modeling of a robot collision with its environment

Modeling of a Neural Pattern Generator with Coupled nonlinear Oscillators

Identification of Three-Dimensional Objects Using Fourier Descriptors of the Boundary Curve