Otago University Research Archive
Browsing Knowledge Engineering Laboratory by Title
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Browsing Knowledge Engineering Laboratory by Title
Now showing items 1-20 of 25
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(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, 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, 2000)The English and Māori word translator ngā aho whakamāori-ā-tuhi was designed to provide single head-word translations to on-line web users. There are over 13,000 words all based on traditional text sources, derived because ... -
(Discussion Paper, 2000)In any data set there some of the data will be bad or noisy. This study identifies two types of noise and investigates the effect of each in the training data of backpropagation neural networks. It also compares the mean ... -
(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, 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)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, 1999)The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, ... -
(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, 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)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 ... -
(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, 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, 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, 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)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, 1996)This paper explores two different methods for improved learning in multimodular fuzzy neural network systems for classification. It demonstrates these methods on a case study of satellite image classification using 3 ... -
(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, 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 ...
Now showing items 1-20 of 25