Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic

Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.

Decision-Tree-Based Classification of Lifetime Maximum Intensity of Tropical Cyclones in the Tropical Western North Pacific

The National Typhoon Center of the Korea Meteorological Administration developed a statistical–dynamical typhoon intensity prediction model for the western North Pacific, the CSTIPS-DAT, using a track-pattern clustering technique. The model led to significant improvements in the prediction of the intensity of tropical cyclones (TCs). However, relatively large errors have been found in a cluster located in the tropical western North Pacific (TWNP), mainly because of the large predictand variance. In this study, a decision-tree algorithm was employed to reduce the predictand variance for TCs in the TWNP. The tree predicts the likelihood of a TC reaching a maximum lifetime intensity greater than 70 knots at its genesis. The developed four rules suggest that the pre-existing ocean thermal structures along the track and the latitude of a TC’s position play significant roles in the determination of its intensity. The developed decision-tree classification exhibited 90.0% and 80.5% accuracy in the training and test periods, respectively. These results suggest that intensity prediction with the CSTIPS-DAT can be further improved by developing independent statistical models for TC groups classified by the present algorithm.

Assessment of genetic divergence of Sesame seeds based on biochemical parameters

The assessment of genetic divergence plays a significant role to identify promising genotypes to initiate crossing programme for crop improvement. The sesame being a nutritious oil seed crop containing various biochemical constituents used as a good dietary choice. The biochemical analysis was done taking freshly harvested seeds of eight (8) parental genotypes and twenty one (21) advance lines. The studies revealed that all the genotypes differed significantly among themselves for all the biochemical parameters including moisture content, oil content, its iodine number and saponification value, both crude and soluble protein, some mineral matters – Ca, P, Mn, Zn, Cu & Fe and Carbohydrate and ascorbic acid content. Mahalanobis generalized genetic distance using D² statistics for the assessment of genetic divergence amongst 29 genotypes based on biochemical parameters. All the genotypes were grouped into eleven distinct clusters. Maximum intra cluster divergence was noted against cluster IX with advance lines having black coloured seeds as its constituents. With regard to inter-cluster distance values the cluster X was mostly divergent from all other clusters and cluster II as the less divergent one from the rest. Cluster X with parental genotype B-14 was identified as superior cluster for the biochemical parameters on the basis of securing higher position for maximum number of characters. Constitution pattern clustering was independent of eco-geographical isolation. Promising segregants with higher oil yielding potentially may yield from inter se mating of advance line 14 and HT-1 with T-12, R-9 or advance lines 10, 14, 20.