102 research outputs found

    Permutation Coding Technique for Image Recognition Systems

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    A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1

    Artificial Neural Networks in Finance Modelling

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    The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to “suggest” how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non- linear models to do forecasting), while in multi-population models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent. A particular application we aim to study is the one regarding “customer profiling”, in which (based on personal and direct relationships) the “buying” behaviour of each customer can be defined, making use of behavioural inference models such as the ones offered by Artificial Neural Networks much better than traditional statistical methodologies.Artificial Neural Network, Financial Modelling, Customer Profiling

    Director's discretionary fund report for FY 1991

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    The Director's Discretionary Fund (DDF) at the Ames Research Center was established to fund innovative, high-risk projects in basic research which would otherwise be difficult to initiate, but which are essential to our future programs. Here, summaries are given of individual projects within this program. Topics covered include scheduling electric power for the Ames Research Center, the feasibility of light emitting diode arrays as a lighting source for plant growth chambers in space, plasma spraying of nonoxide coatings using a constricted arcjet, and the characterization of vortex impingement footprint using non-intrusive measurement techniques

    Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory

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    Hospitality Information Systems: Intuitive, Object-Oriented, and Wireless Technology

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    Automated information system design and implementation is one of the fastest changing aspects of the hospitality industry. During the past several years nothing has increased the professionalism or improved the productivity within the industry more than the application of computer technology. Intuitive software applications, deemed the first step toward making computers more people-literate, object-oriented programming, intended to more accurately model reality, and wireless communications are expected to play a significant role in future technological advancement

    Missileborne Artificial Vision System (MAVIS)

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    Several years ago when INTEL and China Lake designed the ETANN chip, analog VLSI appeared to be the only way to do high density neural computing. In the last five years, however, digital parallel processing chips capable of performing neural computation functions have evolved to the point of rough equality with analog chips in system level computational density. The Naval Air Warfare Center, China Lake, has developed a real time, hardware and software system designed to implement and evaluate biologically inspired retinal and cortical models. The hardware is based on the Adaptive Solutions Inc. massively parallel CNAPS system COHO boards. Each COHO board is a standard size 6U VME card featuring 256 fixed point, RISC processors running at 20 MHz in a SIMD configuration. Each COHO board has a companion board built to support a real time VSB interface to an imaging seeker, a NTSC camera, and to other COHO boards. The system is designed to have multiple SIMD machines each performing different corticomorphic functions. The system level software has been developed which allows a high level description of corticomorphic structures to be translated into the native microcode of the CNAPS chips. Corticomorphic structures are those neural structures with a form similar to that of the retina, the lateral geniculate nucleus, or the visual cortex. This real time hardware system is designed to be shrunk into a volume compatible with air launched tactical missiles. Initial versions of the software and hardware have been completed and are in the early stages of integration with a missile seeker

    Controlled Information Transfer Through An In Vivo Nervous System.

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    The nervous system holds a central position among the major in-body networks. It comprises of cells known as neurons that are responsible to carry messages between different parts of the body and make decisions based on those messages. In this work, further to the extensive theoretical studies, we demonstrate the first controlled information transfer through an in vivo nervous system by modulating digital data from macro-scale devices onto the nervous system of common earthworms and conducting successful transmissions. The results and analysis of our experiments provide a method to model networks of neurons, calculate the channel propagation delay, create their simulation models, indicate optimum parameters such as frequency, amplitude and modulation schemes for such networks, and identify average nerve spikes per input pulse as the nervous information coding scheme. Future studies on neuron characterization and artificial neurons may benefit from the results of our work

    Hybrid approach to the forecasting of electric consumption time series for organizational management in the wholesale market

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    Розглядається проблема підвищення ефективності вирішення комплексу задач прогнозування і планування електроспоживання регіональними компаніями постачальників електроенергії - суб'єктами системи організаційного управління оптовим ринком електроенергії. Проведено аналіз використання різних методів моделювання при вирішенні завдання вибору і побудови моделі прогнозування електроспоживання, формулюється завдання побудови гібридної прогностичної моделі, позбавленої недоліків окремих методів моделювання. Перевага надається підходу, пов'язаного з комплексним використанням математичних засобів на базі апаратів штучних нейронних сіток, генетичного алгоритму і фільтра Калмана для побудови узагальнених нелінійних багатофакторних моделей. Він дозволить підвищити ефективність процесу побудови моделей і їх подальшого використання для пошуку, як короткострокових, так і довгострокових прогнозів. Для виключення впливу випадкових складових часового ряду з нерівномірним розподілом значень показника електроспоживання на процес навчання нейронної сітки як нелінійної моделі прогнозування пропонується попередня її підготовка за допомогою застосування фільтра Калмана. Надалі здійснюється оптимізація топології нейронної сітки на базі генетичного алгоритму, який дозволяє на етапі мутації адаптивно вибирати тип перетворення структури, найбільш підходящий для заданої конфігурації сітки.The problem of increasing the efficiency of solving the complex of tasks of forecasting and planning electric consumption by regional companies of electricity suppliers - subjects of the organizational management system in the wholesale electricity market is considered. The analysis of the use of various modeling methods in solving the problem of choosing and building a model for forecasting electric consumption is carried out. The task of constructing a hybrid prognostic model devoid of the shortcomings of individual modeling methods is formulated. Preference is given to the approach associated with the integrated use of mathematical tools based on apparatus of artificial neural networks, a genetic algorithm and a Kalman filter for constructing generalized nonlinear multifactor models. It will increase the efficiency of the model building process and their subsequent use for searching both short-term and long-term forecasts. In order to eliminate the effect of random components of the time series with an uneven distribution of the values of the electric consumption on the training process of the neural network as a non-linear forecasting model, we suggest its preliminary preparation using the Kalman filter. Further optimization of the neural network topology is carried out on the basis of a genetic algorithm that allows, at the mutation stage, to adaptively choose the type of structure transformation most suitable for a given network configuration.Рассматривается проблема повышения эффективности решения комплекса задач прогнозирования и планирования электропотребления региональными компаниями поставщиков электроэнергии - субъектами системы организационного управления оптовым рынком электроэнергии. Проведен анализ использования различных методов моделирования при решении задачи выбора и построения модели прогнозирования электропотребления, формулируется задача построения гибридной прогностической модели, лишенной недостатков отдельных методов моделирования. Предпочтение отдано подходу, связанному с комплексным использованием математических средств на базе аппаратов искусственных нейронных сетей, генетического алгоритма и фильтра Калмана для построения обобщенных нелинейных многофакторных моделей. Он позволит повысить эффективность процесса построения моделей и их последующего использования для поиска, как краткосрочных, так и долгосрочных прогнозов. Для исключения влияния случайных составляющих временного ряда с неравномерным распределением значений показателя электропотребления на процесс обучения нейронной сети как нелинейной модели прогнозирования предлагается предварительная ее подготовка с помощью применения фильтра Калмана. В дальнейшем осуществляется оптимизация топологии нейронной сети на базе генетического алгоритма, который позволяет на этапе мутации адаптивно выбирать тип преобразования структуры, наиболее подходящий для заданной конфигурации сети

    Intrinsically Evolvable Artificial Neural Networks

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    Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented
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