Ahmad Kalhor

In this paper by using a controller based on a neural network and an estimator, an efficient method in A/F ratio for SI engines is presented. This combined method improves plant performance effectively and provides robustness against disturbances due to work point changing. It is shown that by combining two separate methods, a useful control strategy may be generated. Simulation results reveal the superiority of this method.

In this paper a novel algorithm for structure identification of Takagi-Suegno (TS) fuzzy models based on split and merge clustering is purposed. In this algorithm, by using a sequential split procedure on data space, initial Gaussian functions as constructor blocks are created. By merging these initial blocks, new composite validity functions for locally linear models with a high degree of

... functions, and increase or decrease the number of rules, and the number of inputs adaptively (Kasabov, 2002) and (Angelov & Filev , 2004 ... methods that try to exploit the interpolation ability, structural flexibility and interpretability of fuzzy systems in adaptive learning (Angelo et al ...

Abstract In this paper, we propose a new online predictor model for complex nonlinear processes. The proposed adaptive habitually linear and transiently nonlinear model (AHLTNM) can follow fast and significant structural variations in the process, which is ...

In this paper, we propose a new approach to identify a neuro-fuzzy model. In our approach, data space is partitioned indirectly through a fuzzy clustering method. The clusters are not created directly through spatial features of data points. A gradient vector is defined as major feature of clustering in data space. This feature is estimated for each incoming data points. Creating and updating fuzzy membership functions, adding new clusters and removing redundant clusters are performed through it. Correspond with cluster parameters, fuzzy rules are defined and a neuro-fuzzy model is identified recursively. Prediction of monthly sunspots number is considered to demonstrate the capability of the proposed neuro-fuzzy model.

... structure of weights and neurons permits to approach complex relationships between variables without specifying ... The advantage of using such a function is that the contributions to the input of the ... the LOLIMOT algorithm in the first five iterations for a two dimensional input space ...

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, neuro fuzzy modeling has played a

ABSTRACT Linear trends of a time-varying process include useful and insight data about its temporal behaviors. In this paper, we introduce an approach for extracting the main linear trends of a nonlinear time-varying process. In this approach, originally, an adaptive linear model is utilized to estimate the temporal-linear trends of the process. Then, by using a suitable distance index, an online clustering algorithm has been developed to classify the estimated linear trends. Considering the mean and the number of members for each cluster, main linear trends are extracted for the process. Through an illustrative example, the methodology of the proposed approach in extracting main linear trends is explained and its capability is shown. Also, through two case studies -electrical load time series and pH neutralization process- the application of the proposed method in studying temporal behaviors of processes like stability, changing rate, oscillation and characteristics of transient states are explained.

... functions, and increase or decrease the number of rules, and the number of inputs adaptively (Kasabov, 2002) and (Angelov & Filev , 2004 ... methods that try to exploit the interpolation ability, structural flexibility and interpretability of fuzzy systems in adaptive learning (Angelo et al ...