Otago University Research Archive
Browsing Knowledge Engineering Laboratory by Date Added
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Browsing Knowledge Engineering Laboratory by Date Added
Now showing items 1-20 of 25
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(Discussion Paper, 1998)Biological processes are among the most challenging to predict and control. It has been recognised that the development of an intelligent system for the recognition, prediction and control of process states in a complex, ... -
(Discussion Paper, 1999)The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. ... -
(Discussion Paper, 1999)In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides ... -
(Discussion Paper, 1998)This paper describes combined approaches of data preparation, neural network analysis, and fuzzy inferencing techniques (which we collectively call neuro-fuzzy engineering) to the problem of environmental modelling. The ... -
(Discussion Paper, 1996)Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent ... -
(Discussion Paper, 2000)We report on the clustering of nodes in internally represented acoustic space. Learners of different languages partition perceptual space distinctly. Here, an Evolving Connectionist-Based System (ECOS) is used to model the ... -
(Discussion Paper, 2001)Fuzzy neural networks are connectionist systems that facilitate learning from data, reasoning over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept evolving fuzzy neural networks (EFuNNs), ... -
(Discussion Paper, 2000)In real world information systems, data analysis and processing are usually needed to be done in an on-line, self-adaptive way. In this respect, neural algorithms of incremental learning and constructive network models are ... -
(Discussion Paper, 1997)Fuzzy neural networks provide for the extraction of fuzzy rules from artificial neural network architectures. In this paper we describe a general method, based on statistical analysis of the training data, for the selection ... -
(Discussion Paper, 1998)The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, ... -
(Discussion Paper, 1996)This paper proposes neuro-fuzzy engineering as a novel approach to spatial data analysis and for building decision making systems based on spatial information processing, and the development of this approach by the authors ... -
(Discussion Paper, 1999)The speech recognition field is one of the most challenging fields that has faced scientists for a long time. The complete solution is still far from reach. The efforts are concentrated with huge funds from the companies ... -
(Discussion Paper, 2000)Māori speech data collection and analysis is an ongoing process, as new and existing data sets are continuously accessed for many different experimental speech perception and generation processing tasks. A data management ... -
(Discussion Paper, 2000)In a consensus ensemble all members must agree before they classify a data point. But even when they all agree some data is still misclassified. In this paper we look closely at consistently misclassified data to investigate ... -
(Discussion Paper, 1999)The chapter presents a new methodology for building adaptive, incremental learning systems for image pattern classification. The systems are based on dynamically evolving fuzzy neural networks that are neural architectures ... -
(Discussion Paper, 1996)General Characteristics of the Theme • Emerging technology with rapidly growing practical applications • Nationally and internationally recognised leadership of the University of Otago • Already established organisation ... -
(Discussion Paper, 1998)Variation in fruit maturation can influence harvest timing and duration, post-harvest fruit attributes and consumer acceptability. Present methods of managing and identifying lines of fruit with specific attributes both ... -
(Discussion Paper, 2000)The paper applies novel techniques for on-line, adaptive learning of macroeconomic data and a consecutive analysis and prediction. The evolving connectionist system paradigm (ECOS) is used in its two versions—unsupervised ... -
(Discussion Paper, 1999)This paper discusses the problem of adaptation in automatic speech recognition systems (ASRS) and suggests several strategies for adaptation in a modular architecture for speech recognition. The architecture allows for ... -
(Discussion Paper, 2000)Although widely studied for many years, colour image quantisation remains a challenging problem. We propose to use an evolving self-organising map model for the on-line image quantisation tasks. Encouraging results are ...
Now showing items 1-20 of 25