682 research outputs found
Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning
PhDThis thesis investigates reinforcement learning algorithms suitable for learning
in large state space problems and coevolution. In order to learn in large state
spaces, the state space must be collapsed to a computationally feasible size and
then generalised about. This thesis presents two new implementations of the
classic temporal difference (TD) reinforcement learning algorithm Sarsa that
utilise fuzzy logic principles for approximation, FQ Sarsa and Fuzzy Sarsa. The
effectiveness of these two fuzzy reinforcement learning algorithms is
investigated in the context of an agent marketplace. It presents a practical
investigation into the design of fuzzy membership functions and tile coding
schemas. A critical analysis of the fuzzy algorithms to a related technique in
function approximation, a coarse coding approach called tile coding is given in
the context of three different simulation environments; the mountain-car
problem, a predator/prey gridworld and an agent marketplace. A further
comparison between Fuzzy Sarsa and tile coding in the context of the nonstationary
environments of the agent marketplace and predator/prey gridworld is
presented.
This thesis shows that the Fuzzy Sarsa algorithm achieves a significant reduction
of state space over traditional Sarsa, without loss of the finer detail that the FQ
Sarsa algorithm experiences. It also shows that Fuzzy Sarsa and gradient descent
Sarsa(λ) with tile coding learn similar levels of distinction against a stationary
strategy. Finally, this thesis demonstrates that Fuzzy Sarsa performs better in a
competitive multiagent domain than the tile coding solution
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
We propose a novel scheme for designing fuzzy rule based classifier. An SOFM
based method is used for generating a set of prototypes which is used to
generate a set of fuzzy rules. Each rule represents a region in the feature
space that we call the context of the rule. The rules are tuned with respect to
their context. We justified that the reasoning scheme may be different in
different context leading to context sensitive inferencing. To realize context
sensitive inferencing we used a softmin operator with a tunable parameter. The
proposed scheme is tested on several multispectral satellite image data sets
and the performance is found to be much better than the results reported in the
literature.Comment: 23 pages, 7 figure
Components of Soft Computing for Epileptic Seizure Prediction and Detection
Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection
A Study of recent classification algorithms and a novel approach for biosignal data classification
Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroencephalography (EEG) signals. Similarly, various assistive robotics applications are being developed to interpret eye or muscle signals in humans in order to provide control inputs for external devices. The efficiency for all of these applications depends heavily on being able to process and classify human biosignals. Therefore many techniques from Signal Processing and Machine Learning fields are applied in order to understand human biosignals better and increase the efficiency and success of these applications. This thesis proposes a new classifier for biosignal data classification utilizing Particle Swarm Optimization Clustering and Radial Basis Function Networks (RBFN). The performance of the proposed classifier together with several variations in the technique is analyzed by utilizing comparisons with the state of the art classifiers such as Fuzzy Functions Support Vector Machines (FFSVM), Improved Fuzzy Functions Support Vector Machines (IFFSVM). These classifiers are implemented on the classification of same biological signals in order to evaluate the proposed technique. Several clustering algorithms, which are used in these classifiers, such as K-means, Fuzzy c-means, and Particle Swarm Optimization (PSO), are studied and compared with each other based on clustering abilities. The effects of the analyzed clustering algorithms in the performance of Radial Basis Functions Networks classifier are investigated. Strengths and weaknesses are analyzed on various standard and EEG datasets. Results show that the proposed classifier that combines PSO clustering with RBFN classifier can reach or exceed the performance of these state of the art classifiers. Finally, the proposed classification technique is applied to a real-time system application where a mobile robot is controlled based on person\u27s EEG signal
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