Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Space vector modulation based fast speed response field-orientation control of induction motor drive with adaptive neural integrator

This paper presents a novel fast speed response control strategy for the poly-phase induction motor drive system based on flux angle. The control scheme is derived in rotor field coordinates and employs the estimation of the rotor flux and its position. An adaptive notch filter is proposed to eliminate the dc component of the integration of signals used for the rotor flux estimation. To improve the performance of the rotor flux estimator, derivative term of the back emf is incorporated in the system. The voltage components in the synchronous reference frame are generated in the controllers which are transformed to stationary reference frame for driving the motor. Space vector modulation technique is used here. Simulation of the drive system was carried out and the results were compared with those obtained for a system that produces the above mentioned voltage components using the conventional PI controller. It is observed that the proposed control methodology provides faster response than the conventional PI controller incorporated system.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.