Chaotic maps and pattern recognition - the XOR problem

In this report, we describe a novel application of the Baker's map. We demonstrate that the chaotic properties of this map can be used to implement basic operations in Boolean logic. This observation leads naturally to the possibility of new computational models and implementations for conventional computational systems. Here we show that by considering the variation of the fractal dimension of its attractor, and using varying parameter values as inputs, the generalised Baker's map can be used as a natural exclusive OR (XOR) gate. Further, this map can also be used to create other logical functions such as the AND gate. The efficacy of our results are demonstrated by means of a concrete application; namely by designing, to the best of our knowledge, for the frst time, a half-adder that is constructed entirely by utilising chaotic dynamics

On the training of feedforward neural networks.

by Hau-san Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [178-183]).Chapter 1 --- INTRODUCTIONChapter 1.1 --- Learning versus Explicit Programming --- p.1-1Chapter 1.2 --- Artificial Neural Networks --- p.1-2Chapter 1.3 --- Learning in ANN --- p.1-3Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7Chapter 1.6 --- Incremental Learning --- p.1-10Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORKChapter 2.1 --- The Perceptron --- p.2-1Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEMChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9Chapter 3.4 --- Concluding Remarks --- p.3-15Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKSChapter 4.1 --- Introduction --- p.4-2Chapter 4.2 --- The Radial Basis Function --- p.4-6Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11Chapter 4.5 --- Initialization of the First Node --- p.4-15Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21Chapter 4.10 --- Concluding Remarks --- p.4-33Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKSChapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKSChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden NodesChapter 6.5.1 --- Introduction --- p.6-12Chapter 6.5.2 --- Determination of the Target T-VectorChapter 6.5.2.1 --- Introduction --- p.6-15Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36Chapter 6.11 --- Concluding Remarks --- p.6-50Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEMEChapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2Chapter 7.3 --- The Generalization Measure --- p.7-4Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12Chapter 7.8 --- Concluding Remarks --- p.7-16Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZEChapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6Chapter 8.5 --- Concluding Remarks --- p.8-16Chapter 9 --- CONCLUSIONChapter 9.1 --- Contributions --- p.9-1Chapter 9.2 --- Suggestions for Further Research --- p.9-3REFERENCES --- p.R-1APPENDIX --- p.A-

Adaptive Critic Designs

We discuss a variety of adaptive critic designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming, dual heuristic programming, and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACDs. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACD

An adaptive atmospheric prediction algorithm to improve density forecasting for aerocapture guidance processes

Many modern entry guidance systems depend on predictions of atmospheric parameters, notably atmospheric density, in order to guide the entry vehicle to some desired final state. However, in highly dynamic atmospheric environments such as the Martian atmosphere, the density may vary by as much as 200% from predicted pre-entry trends. This high level of atmospheric density uncertainty can cause significant complications for entry guidance processes and may in extreme scenarios cause complete failure of the entry. In the face of this uncertainty, mission designers are compelled to apply large trajectory and design safety margins which typically drive the system design towards less efficient solutions with smaller delivered payloads. The margins necessary to combat the high levels of atmospheric uncertainty may even preclude scientifically interesting destinations or architecturally useful mission modes such as aerocapture. Aerocapture is a method for inserting a spacecraft into an orbit about a planetary body with an atmosphere without the need for significant propulsive maneuvers. This can reduce the required propellant and propulsion hardware for a given mission which lowers mission costs and increases the available payload fraction. However, large density dispersions have a particularly acute effect on aerocapture trajectories due to the interaction of the high required speeds and relatively low densities encountered at aerocapture altitudes. Therefore, while the potential system level benefits of aerocapture are great, so too are the risks associated with this mission mode in highly uncertain atmospheric environments such as Mars. Contemporary entry guidance systems utilize static atmospheric density models for trajectory prediction and control. These static models are unable to alter the fundamental nature of the underlying state equations which are used to predict atmospheric density. This limits both the fidelity and adaptive freedom of these models and forces the guidance system to retroactively correct for the density prediction errors after those errors have already impacted the trajectory. A new class of dynamic density estimator called a Plastic Ensemble Neural System (PENS) is introduced which is able to generate high fidelity, adaptable density forecast models by altering the underlying atmospheric state equations to better agree with observed atmospheric trends. A new construct called an ensemble echo is also introduced which creates an associative learning architecture, permitting PENS to evolve with increasing atmospheric exposure. The PENS estimator is applied to a numerical guidance system and the performance of the composite system is investigated with over 144,000 guided trajectory simulations. The results demonstrate that the PENS algorithm achieves significant reductions in both the required post-aerocapture performance, and the aerocapture failure rates relative to historical density estimators.Ph.D

More than useable tools: towards an appreciation of Ne?kepmx fibre technology as a significant expression of culture

Prior to the general adoption of manufactured goods brought by traders and settlers to Ne?kepmx territory. Ne?kepmx women spent a great deal of time processing plant materials to make items for daily. ceremonial. and trade purposes, such as baskets. mats, clothing. cradles. rope, and nets, as well as a number of products for decorative or recreational purposes. I call this activity fibre technology. Much of the research concerning the use of plants by Ne?kepmx women in this type of technology was compiled almost one hundred years ago. It offers valuable information about this activity during the nineteenth and early twentieth centuries. Since the body of literature covering this time frame was produced predominantly under the influence of the Boasian anthropological theory of cultural relativity, it describes fibre products mainly by their form, the techniques used, and their utilitarian function. Based on more recent literature about First Nations' cultural practices, that includes a strong Native voice, and on interviews I had with Ne?kepmx women. I argue in this thesis that Ne?kepmx women not only produced useable objects through fibre technology, but that these were works of artistic beauty and also symbolic representations of Ne?kepmx culture. Ne?kepmx women made fibre products with a commitment to respect the spiritual and material worlds at all stages of the process. This is a deep part of Ne?kepmx cultural values, traditional knowledge, and identity. That commitment manifests in beautifully crafted pieces that are distinctly Ne?kepmx. At the same time, through their own ingenuity Ne?kepmx women. both prior to and since colonisation. have adapted fibre products to meet the changing conditions of their own lives. The practice of fibre technology has diminished considerably in the last several decades. Nevertheless, those women who continue to practise it and teach it to others do so with a strong commitment to their traditions in order that fibre technology can remain an important symbolic expression of Ne7kepmx culture.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b121633

216 Jewish Hospital of St. Louis

https://digitalcommons.wustl.edu/bjc_216/1178/thumbnail.jp

The hardware implementation of an artificial neural network using stochastic pulse rate encoding principles

In this thesis the development of a hardware artificial neuron device and artificial neural network using stochastic pulse rate encoding principles is considered. After a review of neural network architectures and algorithmic approaches suitable for hardware implementation, a critical review of hardware techniques which have been considered in analogue and digital systems is presented. New results are presented demonstrating the potential of two learning schemes which adapt by the use of a single reinforcement signal. The techniques for computation using stochastic pulse rate encoding are presented and extended with new novel circuits relevant to the hardware implementation of an artificial neural network. The generation of random numbers is the key to the encoding of data into the stochastic pulse rate domain. The formation of random numbers and multiple random bit sequences from a single PRBS generator have been investigated. Two techniques, Simulated Annealing and Genetic Algorithms, have been applied successfully to the problem of optimising the configuration of a PRBS random number generator for the formation of multiple random bit sequences and hence random numbers. A complete hardware design for an artificial neuron using stochastic pulse rate encoded signals has been described, designed, simulated, fabricated and tested before configuration of the device into a network to perform simple test problems. The implementation has shown that the processing elements of the artificial neuron are small and simple, but that there can be a significant overhead for the encoding of information into the stochastic pulse rate domain. The stochastic artificial neuron has the capability of on-line weight adaption. The implementation of reinforcement schemes using the stochastic neuron as a basic element are discussed

Combined Wavelet-neural Clasifier For Power Distribution Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2002Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2002Bu çalışmada, dağıtım sistemlerinde hibrid “Dalgacık-Yapay Sinir ağı (YSA) tabanlı” bir yaklaşımla arıza sınıflama işlemi gerçeklenmiştir. 34.5 kV “Sağmalcılar-Maltepe” dağıtım sistemi PSCAD/EMTDC yazılımı kullanılarak arıza sınıflayıcı için gereken veri üretilmiştir. Tezin amacı, on farklı kısa-devre sistem arızalarını tanımlayabilecek bir sınıflayıcı tasarlamaktır. Sistemde kullanılan arıza işaretleri 5 kHZ lik örnekleme frekansı ile üretilmiştir. Farklı arıza noktaları ve farklı arıza oluşum açılarındaki hat-akımları ve hat-toprak gerilimlerini içeren sistem arızaları ile bir veritabanı oluşturulmuştur. “Çoklu-çözünürlük işaret ayrıştırma” tekniği kullanılarak altı-kanal akım ve gerilim örneklerinden karakteristik bigi çıkarılmıştır. PSCAD/EMTDC ile üretilen veri bu şekilde bir ön islemden geçirildikten sonra YSA-tabanlı bir yapı ile sınıflama islemi gerçekleştirilmiştir. Bu yapının görevi çeşitli sistem ve arıza koşullarını kapsayan karmaşık arıza sınıflama problemini çözebilmektir. Bu çalışmada, Kohonen’in öğrenme algoritmasını kullanan bir “Kendine-Organize harita” ile “eğitilebilen vektör kuantalama” teknikleri kullanılmıştır. Bu “dalgacık-sinir ağı” tabanlı arıza sınıflayıcı ile eğitim kümesi için % 99-100 arasında ve sınıflayıcıya daha önce hiç verilmemiş test kümesi ile de %85-92 arasında sınıflama oranları elde edilmiştir. Elde edilen başarım oranları literatürdeki sonuçlara yakındır. Geliştirilen birleşik “dalgacık-sinir ağı” tabanlı sınıflayıcı elektrik dağıtım sistemlerindeki arızaların belirlenmesinde iyi sonuçlar vermiş ve iyi bir performans sağlamıştır.In this study an integrated design of fault classifier in a distribution system by using a hybrid “Wavelet- Artificial neural network (ANN) based” approach is implemented. Data for the fault classifier is produced by using PSCAD/EMTDC simulation program on 34.5 kV “Sagmalcılar-Maltepe” distribution system in Istanbul. The objective is to design a classifier capable of recognizing ten classes of three-phase system faults. The signals are generated at an equivalent sampling rate of 5 KHz per channel. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. The characteristic information over six-channel of current and voltage samples is extracted by the “wavelet multi-resolution analysis” technique, which is a preprocessing unit to obtain a small size of interpretable features from the raw data. After preprocessing the raw data, an ANN-based tool was employed for classification task. The main idea in this approach is solving the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. In this project, a self-organizing map, with Kohonen’s learning algorithm and type-one learning vector quantization technique is implemented into the fault classification study. The performance of the wavelet-neural fault classification scheme is found to be around “99-100%” for the training data and around “85-92%” for the test data, which the classifier has not been trained on. This result is comparable to the studied fault classifiers in the literature. Combined wavelet-neural classifier showed a promising future to identify the faults in electric distribution systemsYüksek LisansM.Sc

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion