10 research outputs found
Design of a krill herd algorithm based adaptive channel equalizer
by Saurav Pandey, Rohan Patidar and Nithin V. Georg
A levy interior search algorithm for chaotic system identifications
by Rushi Jariwala, Rohan Patidar and Nithin V. Georg
Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model
An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results.by Akhilesh Gotmare, Rohan Patidar and Nithin V. Georg
On a cuckoo search optimization approach towards feedback system identification
This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study.by Apoorv Patwardhan, Rohan Patidar and Nithin V. Georg
Dynamic nonlinear active noise control: A multi-objective evolutionary computing approach
by Apoorv P.Patwardhan, Rohan Patidar and Nithin V. Georg
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EFFECT OF BIOENZYME SUPPLEMENTATION ON YIELD OF PRETREATED MUNG BEAN SEED
The yield of the crop depends on the growth which is directly controlled by healthy seedling emergence. To get better quality product pretreatment of seed can be a good alternative. The study was designed to analyze the effect of seed pretreatment with silica and fungicide along with foliar spray of bioenzyme on the growth and biochemical parameters of mung bean. The work was undertaken with twelve different treatments given in blocks of 2├Ч 2 m2 area selected by randomized block design. The effect on growth was assessed with the help of different physical parameters and protein, carbohydrate content indicated as biochemical parameters. The concentration of bioenzyme used in the study along with the pretreatment of seeds with silica showed maximum increase in all the studied physical and biochemical parameters. The present work can be used to improve growth at early stage and to enhance biochemical parameters of mung bean
Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review
An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.by Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar and Nithin V. Georg
Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm
by Narendra Kawaria, Rohan Patidar and Nithin V. Georg