Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Date
Now showing items 21-40 of 56
-
Optimisation and modelling of spiking neural networks - Enhancing neural information processing systems through the power of evolution
(LAP LAMBERT Academic Publishing, 2010)Motivated by the desire to better understand the truly remarkable information processing capabilities of the brain, numerous biologically plausible computational models have been explored in the recent decades. Already ... -
Mobile robot navigation - some issues in controller design and implementation
(IEEE Instrumentation and Measurement, Malaysia (IM), 2009) -
Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling
(IEEE, 2009)The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation ... -
Integrated feature and parameter optimization for an evolving spiking neural network
(Springer, 2009)This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following ... -
A PSO based adaboost approach to object detection
(Springer Verlag, 2008)This paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing ... -
A versatile quantum-inspired evolutionary algorithm
(IEEE, 2007)This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature ... -
On-line evolving fuzzy clustering
(IEEE, 2007)In this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership ... -
A novel microarray gene selection method based on consistency
(IEEE Computer Society Press, 2006)Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations ... -
An incremental principal component analysis for chunk data
(IEEE, 2006)This paper presents a new algorithm of dynamic feature selection by extending the algorithm of Incremental Principal Component Analysis (IPCA), which has been originally proposed by Hall and Martin. In the proposed IPCA, ... -
Brain-gene ontology: integrating bioinformatics and neuroinformatics data, information and knowledge to enable discoveries
(IEEE, 2006)The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, ... -
Evolving intelligent systems: methods, learning, & applications
(IEEE, 2006)The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. ... -
Neuro-, genetic-, and quantum inspired evolving intelligent systems
(IEEE, 2006)This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general - computational intelligence (CI) models inspired by principles at different levels of information ... -
Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations
(IEEE, 2006)We present new results from Computational Neurogenetic Modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole ... -
Transductive modeling with GA parameter optimization
(IEEE, 2005)Introduction - While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for ... -
Evolving connectionist systems based role allocation of robots for soccer playing
(IEEE, 2005)For a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile ... -
TWNFC - Transductive neural-fuzzy classifier with weighted data normalization and its application in medicine
(IEEE, 2005)This paper introduces a novel fuzzy model - transductive neural-fuzzy classifier with weighted data normalization (TWNFC), While inductive approaches are concerned with the development of a model to approximate data in the ... -
Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition
(IEEE, 2005)The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is ... -
Incremental learning for online face recognition
(IEEE, 2005)In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space of input variables is learned incrementally to adapt to incoming training samples. A benefit of this ... -
A computational neurogenetic model of a spiking neuron
(IEEE, 2005)The paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal ... -
NFI: a neuro-fuzzy inference method for transductive reasoning
(IEEE, 2005)This paper introduces a novel neural fuzzy inference method - NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS - dynamic neuro-fuzzy inference systems for both online and offline time ...