Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
Now showing items 48-56 of 56
-
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 ... -
Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks
(InTech, 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 ... -
Robotics for engineering education
(Robocup - Singapore, 2010)Most products are the integration of modules from different engineering areas – mechanical, electrical and electronics, computing etc. Engineering graduates are expected to design, manufacture and control those ... -
The Role of Event Related Potentials in Pre-comprehension Processing of Consumers to Marketing Logos
(Guilan University of Medical Sciences, and co-published by Negah Institute for Scientific Communication, 2019)Background: In human behavior study, by peering directly into the brain and assessing distinct patterns, evoked neurons and neuron spike can be more understandable by taking advantages of accurate brain analysis. Objectives: ... -
SPAN: Spike Pattern Association Neuron for learning spatio-temporal sequences
(World Scientific Publishing Company, 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 ... -
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 ... -
Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition
(IEEE, 2004)This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is ... -
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 ... -
WDN-RBF: weighted data normalization for radial basic function type neural networks
(IEEE, 2004)This paper introduces an approach of Weighted Data Normalization (WDN) for Radial Basis Function (RBF) type of neural networks. It presents also applications for medical decision support systems. The WDN method optimizes ...