A Sequential Circuits Test Set Generation Method Based on Ant Colony Particle Swarmalgorithm

Ant Colony Optimizations for Initialization of Synchronous Sequential Circuits

2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis ◽

10.1109/cas-ictd.2009.4960786 ◽

2009 ◽

Cited By ~ 1

Author(s):

Xiaojing Hu ◽

Zhengxiang Song ◽

Jianhua Wang ◽

Yingsan Geng ◽

Wang Shen

Keyword(s):

Sequential Circuits ◽

Ant Colony ◽

Synchronous Sequential Circuits

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Test set partitioning and dynamic fault dictionaries for sequential circuits

Proceedings of 1993 IEEE 2nd Asian Test Symposium (ATS) ATS-93 ◽

10.1109/ats.1993.398799 ◽

2002 ◽

Cited By ~ 1

Author(s):

P.G. Ryan ◽

W.K. Fuchs

Keyword(s):

Sequential Circuits ◽

Set Partitioning ◽

Test Set

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Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch006 ◽

2020 ◽

pp. 82-95

Author(s):

Ashraf M. Abdelbar ◽

Islam Elnabarawy ◽

Donald C. Wunsch II ◽

Khalid M. Salama

Keyword(s):

Neural Network ◽

Neural Networks ◽

Ant Colony Optimization ◽

High Order ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Test Set ◽

Benchmark Datasets ◽

Gradient Based ◽

High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.

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Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

Advances in Computational Intelligence and Robotics - Applied Artificial Higher Order Neural Networks for Control and Recognition ◽

10.4018/978-1-5225-0063-6.ch014 ◽

2016 ◽

pp. 362-374 ◽

Cited By ~ 4

Author(s):

Ashraf M. Abdelbar ◽

Islam Elnabarawy ◽

Donald C. Wunsch II ◽

Khalid M. Salama

Keyword(s):

Neural Network ◽

Neural Networks ◽

Ant Colony Optimization ◽

High Order ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Test Set ◽

Benchmark Datasets ◽

Gradient Based ◽

High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOR, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOR to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOR has better test set generalization than R-Prop, though not to a statistically significant extent.

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Comparative Analysis of Determinant Elimination Method, Ant Colony Algorithms and Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.624.509 ◽

2014 ◽

Vol 624 ◽

pp. 509-511

Author(s):

Xin Fu

Keyword(s):

Genetic Algorithm ◽

Comparative Analysis ◽

Optimization Algorithm ◽

Ant Colony ◽

Optimal Solutions ◽

Optimal Test ◽

Test Set ◽

Elimination Method ◽

Ant Colony Algorithms ◽

Test Sets

In this paper, the analysis of optimizing test sets of which the optimal solutions are already known is made first. Then the optimization results and execution time of Determinant Elimination Method, Ant Colony Algorithms as well as Genetic Algorithm are compared. At last, Based on the concept of optimal test set proposed in this paper, plenty of test sets which need to be optimized are randomly generated. The optimization algorithm proposed in this paper is also used to optimize the test sets and the consequences of optimization is desirable.

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3-weight pseudo-random test generation based on a deterministic test set for combinational and sequential circuits

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/43.238041 ◽

1993 ◽

Vol 12 (7) ◽

pp. 1050-1058 ◽

Cited By ~ 21

Author(s):

I. Pomeranz ◽

S.M. Reddy

Keyword(s):

Test Generation ◽

Sequential Circuits ◽

Test Set ◽

Random Test ◽

Combinational And Sequential Circuits

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Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

PsycEXTRA Dataset ◽

10.1037/e527382013-008 ◽

2012 ◽

Author(s):

Earth B. Ugat ◽

Jennifer Joyce M. Montemayor ◽

Mark Anthony N. Manlimos ◽

Dante D. Dinawanao

Keyword(s):

Ant Colony Optimization ◽

Ant Colony ◽

Timetabling Problem ◽

University Course ◽

The University

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Using Connectionist Modules for Decision Support

Methods of Information in Medicine ◽

10.1055/s-0038-1634790 ◽

1990 ◽

Vol 29 (03) ◽

pp. 167-181 ◽

Cited By ~ 6

Author(s):

G. Hripcsak

Keyword(s):

Decision Support ◽

Standard Deviation ◽

Confidence Interval ◽

Posterior Probability ◽

Back Propagation ◽

Connectionist Model ◽

Test Set ◽

The Third ◽

Independent Test ◽

Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

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