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Rough-Neural Computing: An Introduction

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Book cover Rough-Neural Computing

Part of the book series: Cognitive Technologies ((COGTECH))

Summary

This chapter presents a new paradigm for neural computing that has its roots in rough set theory. Historically, this paradigm has three main threads: production of a training set description, calculus of granules, and interval analysis. This paradigm gains its inspiration from the work of Pawlak on rough set philosophy as a basis for machine learning and from work on data mining and pattern recognition by Swiniarski and others in the early 1990s. The focus of this work is on the production of a training set description and inductive learning using knowledge reduction algorithms. This first thread in rough-neural computing has a strong presence in current neural computing research. The second thread in rough-neural computing has two main components: information granule construction in distributed systems of agents and local parameterized approximation spaces (see Sect. 2.2 and Chap. 3). A formal treatment of the hierarchy of relations of being a part to a degree (also known as approximate rough mereology) was introduced by Polkow ski and Skowron in the mid-and late-1990s. Approximate rough mereology provides a basis for an agent-based, adaptive calculus of granules. This calculus serves as a guide in designing rough-neural computing systems. A number of touchstones of rough-neural computing have emerged from efforts to establish the foundations for granular computing: cooperating agent, granule, granule measures (e.g., inclusion, closeness), and approximation space parameter calibration. The notion of a cooperating agent in a distributed system of agents provides a model for a neuron. Information granulation and granule approximation define two principal activities of a neuron. Included in the toolbox of an agent (neuron) are measures of granule inclusion and closeness of granules.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700108, India

    Sankar K. Pal

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada

    James F. Peters

  3. Polish-Japanese Institute of Information Technology Research Center, Koszykowa 86, Warsaw, 02-008, Poland

    Lech Polkowski

  4. Department of Mathematics and Computer Science, University of Warmia and Mazury, Żołnierska 14a, Olsztyn, 10-561, Poland

    Lech Polkowski

  5. Institute of Mathematics, Warsaw University, Banacha 2, Warsaw, 02-097, Poland

    Andrzej Skowron

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  1. Sankar K. Pal
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Editors and Affiliations

  1. Indian Statistical Institute, Machine Intelligence Unit, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal

  2. Department of Mathematics and Computer Science, University of Warmia and Mazury, Zolnierska 14, Olsztyn, Poland

    Lech Polkowski

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Pal, S.K., Peters, J.F., Polkowski, L., Skowron, A. (2004). Rough-Neural Computing: An Introduction. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-18859-6_2

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