Advances in the Dempster-Shafer theory of evidence
Comparison of neofuzzy and rough neural networks
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Digital Image Processing
Rough-Neuro-Computing: Techniques for Computing with Words
Rough-Neuro-Computing: Techniques for Computing with Words
Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation
IEEE Transactions on Knowledge and Data Engineering
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Wireless Agent Guidance of Remote Mobile Robots: Rough Integral Approach to Sensor Signal Analysis
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Neural Networks Design: Rough Set Approach to Continuous Data
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Measures of Inclusion and Closeness of Information Granules: A Rough Set Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Closeness of Performance Map Information Granules: A Rough Set Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Rough Neural Network for Software Change Prediction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Controller Design for the Khepera Robot: A Rough Set Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
Rough set uncertainty for robotic systems
Journal of Computing Sciences in Colleges
Rough sets: trends and challenges
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Adaptive granular control of an HVDC system: a rough set approach
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
A Controller Design for the Khepera Robot: A Rough Set Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
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This chapter considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. This paradigm for neurocomputing that has its roots in rough set theory, works well in cases where there is uncertainty about the values of measurements used to make decisions. In the case of the line-crawling robot (LCR) described in this chapter, rough neurocomputing works very well in classifying noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electro-magnetic field surrounding conductors. In rough neurocomputing, training a network of neurons is defined by algorithms for adjusting parameters in the approximation space of each neuron. Learning in a rough neural network is defined relative to local parameter adjustments. Input to a sensor signal classifier is in the form of clusters extracted from convex hulls that "enclose" similar sensor signal values. This chapter gives a fairly complete description of a LCR that has been developed over the past three years as part of a Manitoba Hydro research project. This robot is useful in solving maintenance problems in power systems. A description of the locomotion features of a line-crawling robot and the basic architecture of a rough neurocomputing system for robot navigation are given. A brief description of the fundamental features of rough set theory used in the design of a rough neural network is included in this chapter. A sample sensor signal classification experiment using a recent implementation of rough neural networks is also given.