THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

/pdf/evolving-artificial-neural-networks-4kzrbu7pkn.pdf

Evolving artificial neural networks

List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index.

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

A model is presented for a neural network with competitive learning that demonstrates the self-organizing capabilities arising from the inclusion of a simple temporal inhibition mechanism within the neural units. This mechanism consists of the inhibition, for a certain time, of the neuron that generates an action potential; such a process is termed Post_Fire inhibition. The neural inhibition period, or degree of inhibition, and the way it is varied during the learning process, represents a decisive factor in the behaviour of the network, in addition to constituting the main basis for the exploitation of the model. Specifically, we show how Post_Fire inhibition is a simple mechanism that promotes the participation of and cooperation between the units comprising the network; it produces self-organized neural responses that reveal spatio–temporal characteristics of input data. Analysis of the inherent properties of the Post_Fire inhibition and the examples presented show its potential for applications such as vector quantization, clustering, pattern recognition, feature extraction and object segmentation. Finally, it should be noted that the Post_Fire inhibition mechanism is treated here as an efficient abstraction of biologically plausible mechanisms, which simplifies its implementation.

/pdf/self-organization-by-temporal-inhibition-soti-1ez9b8jcb6.pdf

Self-Organization by Temporal Inhibition (SOTI)

In this paper, a new approach for Self-evolving PArameter-free fuzzy Rule-based Controller (SPARC) is proposed. Two illustrative examples are provided aiming a proof of concept. The proposed controller can start with no pre-defined fuzzy rules, and does not need to pre-define the range of the output or control variables. This SPARC learns autonomously from its own actions while performing the control of the plant. It does not use any parameters, explicit membership functions, any off-line pre-training nor the explicit model (e.g. in a form of differential equations) of the plant. It combines the relative older concept of indirect adaptive control with the newer concepts of (self-)evolving fuzzy rule-based systems (and controllers, in particular) and with the very recent concept of parameter-free, data cloud and data density based fuzzy rule based systems (and controllers in particular). It has been demonstrated that a fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hyper-surface acting as a data space) it is possible generate a parameter-free control structure and evolve it in on-line mode. Moreover, the results demonstrate that this autonomous controller is effective (has comparative error and performance characteristics) to other known controllers, including self-learning ones, but surpasses them with its flexibility and extremely lean structure (small number of prototypes/focal points which serve as seeds to form parameter-free and membership function-free fuzzy rules based on them). The illustrative examples aim primarily proof of concept.

Self-evolving parameter-free Rule-based Controller:SPARC

This document defines a subscription and push mechanism for
datastores. This mechanism allows client applications to request
updates from a datastore, which are then pushed by the server to the
client per a subscription policy, without requiring additional client
requests.

Subscribing to datastore push updates

The realization of a control unit can be done using a complex circuitry or microprogramming. The latter may be considered as an alternative method of implementation of machine instructions that can reduce the complexity and increase the flexibility of the control unit. The microcode efficiency and speed are of vital importance for the computer to execute machine instructions fast. This is a difficult task and it requires expert knowledge. It would be interesting and very helpful to have automated tools that, given a machine instruction description, could generate an efficient and correct microprogram. A good option is to use evolutionary computation techniques, which have proved been effective in the evolution of computer programs. In this paper, we intend to show how evolutionary computing techniques could be used to face this problem of generating efficient microprograms. We have developed a microarchitecture simulator of a real machine in order to evaluate an individual and to assign it the fitness value (to determine whether this candidate solution correctly implements the instruction machine). The proposed method is successful in generating correct solutions, not only for the machine code instruction set, but for new machine instructions not included in such set. We have shown that our approach can generate microprogramms to execute (to schedule microinstructions) the machine level instructions for a real machine. Moreover, this evolutive method could be applied to any microarchitecture just by changing the microinstruction set and pre-conditions of each machine instruction to guide evolution.

Evolving machine microprograms

A synthesis procedure is described for multivalued threshold decoders and their application to the implementation of multivalued functions. This method is based on the definition of inversion and extreme functions. Decoders are obtained using only the very simple blocks which synthesize inversion functions. This general design procedure may be used for any integrated circuit technology. As an example, the method is applied to the synthesis of an integrated CMOS quaternary decoder. Both the layout and the timing of the integrated circuit, and their application to the synthesis of multivalued functions and multistable memory elements, are shown.

Alberto Prieto

Papers

Self-Organization by Temporal Inhibition (SOTI)

Self-evolving parameter-free Rule-based Controller:SPARC

Subscribing to datastore push updates

Evolving machine microprograms

Implementation and applications of multivalued decoders