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Analysis of the macroeconomic development of European and Asia-Pacific countries with the use of connectionist models
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 ...
From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition
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 ...
Improved learning strategies for multimodular fuzzy neural network systems: a case study on image classification
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 ...
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
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 ...
Neuro-fuzzy engineering for spatial information processing
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 ...
A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process
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, ...
Comparing Huber’s M-Estimator function with the mean square error in backpropagation networks when the training data is noisy
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 ...
Using consensus ensembles to identify suspect data
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 ...
Looking for a new AI paradigm: Evolving connectionist and fuzzy connectionist systems—Theory and applications for adaptive, on-line intelligent systems
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, ...
Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition
The paper is a study on a new class of spatial-temporal evolving fuzzy neural network systems (EFuNNs) for on-line adaptive learning, and their applications for adaptive phoneme recognition. The systems evolve through ...