- Scholarly Commons Home
- →
- AUT University Research
- →
- KEDRI - the Knowledge Engineering and Discovery Research Institute
- →
- Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Date
JavaScript is disabled for your browser. Some features of this site may not work without it.
Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Date
Now showing items 1-20 of 40
-
(World Scientific Publishing Company, AUT University; 2012)Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for ... -
(Springer, AUT University; 2011)Stroke is a major cause of disability and mortality in most economically developed countries that increasing global importance. Up till now, there is uncertainty regarding the effect of weather conditions on stoke occurrence. ... -
(AUT University; 2011)We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple ... -
(AUT University; 2011)This study employs networks of stochastic spiking neurons as reservoirs for liquid state machines (LSM). We experimentally investigate the separation property of these reservoirs and show their ability to generalize classes ... -
(AUT University; 2011)Computational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction ... -
(InTech, AUT University; 2011)Introduction: Particle Swarm Optimization (PSO) was introduced in 1995 by Russell Eberhart and James Kennedy (Eberhart & Kennedy, 1995). PSO is a biologically-inspired technique based around the study of collective behaviour ... -
![[File]](/themes/AUT/images/mime.png)
(LAP LAMBERT Academic Publishing, AUT University; 2010) -
(AUT University, 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 ... -
(AUT University, 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 ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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, ... -
(IEEE, AUT University; 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, ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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. ... -
(IEEE Computer Society Press, AUT University; 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 ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 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 ...
Now showing items 1-20 of 40