This paper summarizes the work that was done to explore the use a system based approach to generate test vectors for electronic systems. The process that was developed takes advantage of an unsupervised neural network algorithm Adaptive Resonance Theory (ART) and a Genetic Algorithm (GA) that is combined to form an optimal control system. The GA generates a population of test patterns (individuals). Each individual is provided as timed inputs to a set of behavior based simulations representing good and faulty circuits. The response of each model is recombined in the form of an image matrix with each row representing a signature of each of the different circuits. FuzzyART provides a method of image recognition, extracting those images that are distinctly different from any other. Each individual generated by the GA is evaluated and a fitness is provided by FuzzyART by the number of neuron clusters formed. New test sequences evolve with increasing fault isolation and detection. The process is repeated until a maximum number of models have been identified and separated. A selective breading algorithm was included to reduce the need for large populations, thus increasing the speed to converge to the `best test.'
The main objective of this work was to investigate the use of 'sensor based real time decision and control technology' applied to actively control the arrestment of aircraft (manned or unmanned). The proposed method is to develop an adaptively controlled system that would locate the aircraft's extended tailhook, predict its position and speed at the time of arrestment, adjust an arresting end effector to actively mate with the arresting hook and remove the aircraft's kinetic energy, thus minimizing the arresting distance and impact stresses. The focus of the work presented in this paper was to explore the use of fuzzy adaptive resonance theorem (fuzzy art) neural network to form a MSI scheme which reduces image data to recognize incoming aircraft and extended tailhook. Using inputs from several image sources a single fused image was generated to give details about range and tailhook characteristics for an F18 naval aircraft. The idea is to partition an image into cells and evaluate each using fuzzy art. Once the incoming aircraft is located in a cell that subimage is again divided into smaller cells. This image is evaluated to locate various parts of the aircraft (i.e., wings, tail, tailhook, etc.). the cell that contains the tailhook provides resolved position information. Multiple images from separate sensors provides opportunity to generate range details overtime.
KEYWORDS: Neurons, Neural networks, Diagnostics, Digital electronics, System identification, Data modeling, Circuit switching, Clocks, Analog electronics, Logic
This work, investigated the use of Neural Network technology to simulate faults and to generate input/output patterns used to diagnose electronic circuits via pattern classification. There are several types of circuits (i.e., digital, analog, hybrid (digit-analog), RF, and microwave). This study focused on digital circuits while maintaining the posture of considering other types in the future with similar solutions. The main focus was to investigate a methodology to model complex digital components using system identification neural network architectures. Using those components in software, a digital circuit was assembled. Faults indicating stuck at 1 or 0 was propagated through the circuit (one at a time). Input and output sequences were combined for each situations modeled and those sequences were classified to the known modeled behavior using the Adaptive Resonance Theory neural network algorithm. In addition, a data reduction methodology was established to generate input patterns required for the recognition scheme.
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