NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously

On the analysis of the Hopfield network : a geometric approach

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Bibliography: leaves 56-58.by Mohamad A. Akra.M.S

An analog neuromime suitable for VLSI implementation

The continuous-time mathematics of a binary-decision making neuromime are presented. The model, generally describable as a simplification of the Hodgkin-Huxley model, seeks to mimic many properties of biological neurons including spatial and temporal summation of excitatory and inhibitory inputs, action potential evolution controlled by countervailing charge movements, and postinhibitory rebound and accommodation. A nonlinear, deterministic, state-variable model is developed and then extended into a non-linear stochastic model which seeks to capture the stochastic properties of natural neurons. Finally, a linearized state-variable model is developed for the statistical evolution of single-units; that analytic model should prove useful for studying interconnected networks of stochastic neuromimes

Proposal for an analog CMOS median filter system based on neural network architectural principles

This thesis summarizes the investigation of a proposed analog electronic CMOS system for performing median filtering. A description of the problem and rational for investigating neural networks are given followed by a review of recent efforts toward solving the median filtering problem in hardware. A review of the major developments in hardware neural networks is also presented followed by the system proposal. A comparator design intended to function as a major building block is presented and analyzed. A description of efforts to accurately model the comparator follows. A Spice macro model simulation was assembled as well as a dedicated Runge-Kutta system level simulation. The two models were used to evaluate the system's performance when asked to perform median filtering on a number of different types of input data sets. Methods for predicting the behavior of the system are proposed and compared to simulation results. Finally, conclusions and suggestions for future investigations are offered based on the reported simulation results. A large amount of time was spent on putting the necessary software in place to do the work that this thesis summarizes. Difficulties with incompatible spice models, curve fitters. pre-production software versions, and communication links between computers abounded. In spite of all these obstacles, some meaningful data was finally generated allowing the conclusion of this effort.Electrical Engineerin

FEEDFORWARD ARTIFICIAL NEURAL NETWORK DESIGN UTILISING SUBTHRESHOLD MODE CMOS DEVICES

This thesis reviews various previously reported techniques for simulating artificial neural networks and investigates the design of fully-connected feedforward networks based on MOS transistors operating in the subthreshold mode of conduction as they are suitable for performing compact, low power, implantable pattern recognition systems. The principal objective is to demonstrate that the transfer characteristic of the devices can be fully exploited to design basic processing modules which overcome the linearity range, weight resolution, processing speed, noise and mismatch of components problems associated with weak inversion conduction, and so be used to implement networks which can be trained to perform practical tasks. A new four-quadrant analogue multiplier, one of the most important cells in the design of artificial neural networks, is developed. Analytical as well as simulation results suggest that the new scheme can efficiently be used to emulate both the synaptic and thresholding functions. To complement this thresholding-synapse, a novel current-to-voltage converter is also introduced. The characteristics of the well known sample-and-hold circuit as a weight memory scheme are analytically derived and simulation results suggest that a dummy compensated technique is required to obtain the required minimum of 8 bits weight resolution. Performance of the combined load and thresholding-synapse arrangement as well as an on-chip update/refresh mechanism are analytically evaluated and simulation studies on the Exclusive OR network as a benchmark problem are provided and indicate a useful level of functionality. Experimental results on the Exclusive OR network and a 'QRS' complex detector based on a 10:6:3 multilayer perceptron are also presented and demonstrate the potential of the proposed design techniques in emulating feedforward neural networks

Communications and control for electric power systems: Power system stability applications of artificial neural networks

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed