Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks

The main architectures, learning abilities and applications of radial basis function (RBF) neural networks are well documented. However, to the best of our knowledge, no in-depth analyses have been carried out into the influence on the behaviour of the neural network arising from the use of different alternatives for the design of an RBF (different non-linear functions, distances, number of neurons, structures, etc.). Thus, as a complement to the existing intuitive knowledge, it is necessary to have a more precise understanding of the significance of the different alternatives. In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis Of the VAriance (ANOVA). In order to obtain results that are widely applicable, various problems of classification, functional approximation and time series estimation are analyzed. Conclusions are drawn regarding the whole set.     

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

This paper presents a fast and new deterministic model selection methodology for incremental radial basis function neural network (RBFNN) construction in time series prediction problems. The development of such special designed methodology is motivated by the problems that arise when using a K-fold cross-validation-based model selection methodology for this paradigm: its random nature and the subjective decision for a proper value of K, resulting in large bias for low values and high variance and computational cost for high values. Taking into account these drawbacks, the proposed model selection approach is a combined algorithm that takes advantage of two balanced and representative training and validation sets for their use in RBFNN initialization, optimization and network model evaluation. This way, the model prediction accuracy is improved, getting small variance and bias, reducing the computation time spent in selecting the model and avoiding random and computationally expensive model selection methodologies based on K-fold cross-validation procedures.     

Contents,Volume 12, 2000

Volume Contents

Contents, Volume 12, 2000     

Daily living activity recognition based on statistical feature quality group selection

The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one-feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist.     

Online global learning in direct fuzzy controllers

A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.     

Automatic PC disassembly for component recovery

In this article, a personal computer disassembly cell is presented. With this cell, a certain degree of automatism is afforded for the non-destructive disassembly process and for the recycling of these kinds of mass-produced electronic products. Each component of the product can be separated. The disassembly cell is composed of several sub-systems, each of which is dedicated to the planning and execution of one type of task. A computer vision system is employed for the recognition and localisation of the product and of each of its components. The disassembly system proposed here also has a modelling system for the products and each of its components, the information necessary for the planning of tasks, generating the disassembly sequence and planning of the disassembly movements. These systems co-operate with each other to achieve a semi-automatic disassembly of the product.     

Parallel dynamic water supply scheduling in a cluster of computers

The parallelization of complex planning and control problems arising in diverse application areas in the industrial, services and commercial environments not only allows the determination of control variables in the required times but also improves the performance of the control procedure as more processors are involved in the execution of the parallel program. In this paper we describe a scheduling application in a water supply network to demonstrate the benefits of parallel processing. The procedure we propose combines dynamic programming with genetic algorithms and time series prediction in order to solve problems in which decisions are made in stages, and the states and control belong to a continuous space. Taking into account the computational complexity of these applications and the time constraints that are usually imposed, the procedure has been implemented by a parallel program in a cluster of computers, an inexpensive and widely extended platform that can make parallelism a practical means of tackling complex problems in many different environments. Copyright © 2001 John Wiley & Sons, Ltd.     

Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition

Neural Processing Letters - The recognition of human activity has been deeply explored during the recent years. However, most proposed solutions are mainly devised to operate in ideal conditions,...     

Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning

Neural Processing Letters - Recognizing human activities seamlessly and ubiquitously is now closer than ever given the myriad of sensors readily deployed on and around users. However, the training...