1,049 research outputs found

    Pattern Classification in Dynamic Environments: Tagged Feature-Class Representation and the Classifiers

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    he classifiers characterized by a tagged feature-class representation, a univariate discrimination approach, a cooperative classification scheme, and a logic-based learning strategy are discussed. Neither of the classifiers bears the constraints to the fixed sets of features and classes. Concepts of the tagged feature-class representation and the properties of feature matching in the dynamic environment are studied. Experimental tests and results of the classifiers are illustrated

    A Novel Clustering Algorithm Based on Quantum Games

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    Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.Comment: 19 pages, 5 figures, 5 table

    Stochastic arrays and learning networks

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    This thesis presents a study of stochastic arrays and learning networks. These arrays will be shown to consist of simple elements utilising probabilistic coding techniques which may interact with a random and noisy environment to produce useful results. Such networks have generated considerable interest since it is possible to design large parallel self-organising arrays of these elements which are trained by example rather than explicit instruction. Once the learning process has been completed, they then have the potential ability to form generalisations, perform global optimisation of traditionally difficult problems such as routing and incorporate an associative memory capability which can enable such tasks as image recognition and reconstruction to be performed, even when given a partial or noisy view of the target. Since the method of operation of such elements is thought to emulate the basic properties of the neurons of the brain, these arrays have been termed neural 'networks. The research demonstrates the use of stochastic elements for digital signal processing by presenting a novel systolic array, utilising a simple, replicated cell structure, which is shown to perform the operations of Cyclic Correlation and the Discrete Fourier Transform on inherently random and noisy probabilistic single bit inputs. This work is then extended into the field of stochastic learning automata and to neural networks by examining the Associative Reward-Punish (A(_R-P)) pattern recognising learning automaton. The thesis concludes that all the networks described may potentially be generalised to simple variations of one standard probabilistic element utilising stochastic coding, whose properties resemble those of biological neurons. A novel study is presented which describes how a powerful deterministic algorithm, previously considered to be biologically unviable due to its nature, may be represented in this way. It is expected that combinations of these methods may lead to a series of useful hybrid techniques for training networks. The nature of the element generalisation is particularly important as it reveals the potential for encoding successful algorithms in cheap, simple hardware with single bit interconnections. No claim is made that the particular algorithms described are those actually utilised by the brain, only to demonstrate that those properties observed of biological neurons are capable of endowing collective computational ability and that actual biological algorithms may perhaps then become apparent when viewed in this light

    Reinforcement Learning

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    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

    The 4th Conference of PhD Students in Computer Science

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    Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems

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    Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty
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