8 results
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2. Bootstrap Learning and Visual Processing Management on Mobile Robots.
- Author
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Sridharan, Mohan
- Subjects
ROBOTICS ,TECHNOLOGICAL innovations ,ALGORITHMS ,DECISION making ,MOBILE robots ,GLOBAL environmental change - Abstract
A central goal of robotics and AI is to enable a team of robots to operate autonomously in the real world and collaborate with humans over an extended period of time. Though developments in sensor technology have resulted in the deployment of robots in specific applications the ability to accurately sense and interact with the environment is still missing. Key challenges to the widespread deployment of robots include the ability to learn models of environmental features based on sensory inputs, bootstrap off of the learned models to detect and adapt to environmental changes, and autonomously tailor the sensory processing to the task at hand. This paper summarizes a comprehensive effort towards such bootstrap learning, adaptation, and processing management using visual input. We describe probabilistic algorithms that enable a mobile robot to autonomously plan its actions to learn models of color distributions and illuminations. The learned models are used to detect and adapt to illumination changes. Furthermore, we describe a probabilistic sequential decision-making approach that autonomously tailors the visual processing to the task at hand. All algorithms are fully implemented and tested on robot platforms in dynamic environments. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
3. Unsupervised Topographic Learning for Spatiotemporal Data Mining.
- Author
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Cabanes, Guénaël and Bennani, Younès
- Subjects
DATA mining ,RADIO frequency identification systems ,DATABASE searching ,ALGORITHMS ,ROBOTICS ,COMPUTER vision ,MOBILE computing - Abstract
In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
4. Access Network Selection Based on Fuzzy Logic and Genetic Algorithms.
- Author
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Alkhawlani, Mohammed and Ayesh, Aladdin
- Subjects
WIRELESS communications ,RADIO (Medium) ,QUALITY of service ,FUZZY logic ,GENETIC algorithms ,SCALABILITY ,COMPUTER networks ,ALGORITHMS ,PRODUCT quality - Abstract
In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
5. Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification.
- Author
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Salih, Dhiadeen Mohammed, Noor, Samsul Bahari Mohd, Hamiruce Merhaban, Mohammad, and Kamil, Raja Mohd
- Subjects
WAVELETS (Mathematics) ,MACHINE learning ,ALGORITHMS ,NONLINEAR systems ,DYNAMICAL systems - Abstract
A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
6. Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction.
- Author
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Chao-Hong Chen and Ying-ping Chen
- Subjects
PARTICLE swarm optimization ,STOCHASTIC convergence ,STOCHASTIC analysis ,STATISTICS ,ALGORITHMS ,ESTIMATION theory - Abstract
We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
7. Where Artificial Intelligence and Neuroscience Meet: The Search for Grounded Architectures of Cognition.
- Author
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van der Velde, Frank
- Subjects
ARTIFICIAL intelligence ,NEUROSCIENCES ,COGNITION ,SENSORY evaluation ,PHYSIOLOGY ,ALGORITHMS - Abstract
The collaboration between artificial intelligence and neuroscience can produce an understanding of the mechanisms in the brain that generate human cognition. This article reviews multidisciplinary research lines that could achieve this understanding. Artificial intelligence has an important role to play in research, because artificial intelligence focuses on the mechanisms that generate intelligence and cognition. Artificial intelligence can also benefit from studying the neural mechanisms of cognition, because this research can reveal important information about the nature of intelligence and cognition itself. I will illustrate this aspect by discussing the grounded nature of human cognition. Human cognition is perhaps unique because it combines grounded representations with computational productivity. I will illustrate that this combination requires specific neural architectures. Investigating and simulating these architectures can reveal how they are instantiated in the brain. The way these architectures implement cognitive processes could also provide answers to fundamental problems facing the study of cognition. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
8. A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion.
- Author
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Kao, Johnny W. H., Berber, Stevan M., and Bigdeli, Abbas
- Subjects
ALGORITHMS ,ARTIFICIAL neural networks ,MATHEMATICAL models ,DECODERS & decoding ,ENCODING ,ERROR rates ,DATA transmission systems ,DIGITAL signal processing ,MATHEMATICS - Abstract
A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
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