Initial costates derived by near-optimal reference sequence and least-squares method

In this paper,we present a novel initial costates solver for initializing time-optimal trajectory problems in relative motion with continuous low thrust.The proposed solver consists of two primary components:training a Multilayer Perceptron(MLP)for generating reference sequence and Time of Flight(TOF)to the target,and deriving a system of linear algebraic equations for obtaining the initial costates.To overcome the challenge of generating training samples for the MLP,the backward generation method is proposed to obtain five different training databases.The training database and sample form are determined by analyzing the input and output correlation using the Pearson correlation coefficient.The best-performing MLP is obtained by analyzing the training results with various hyper-parameter combinations.A reference sequence starting from the initial states is obtained by integrating forward with the near-optimal control vector from the output of MLP.Finally,a system of linear algebraic equations for estimating the initial costates is derived using the reference sequence and the necessary conditions for optimality.Simulation results demonstrate that the proposed initial costates solver improves the convergence ratio and reduce the function calls of the shooting function.Furthermore,Monte-Carlo simulation illustrates that the initial costates solver is applicable to different initial velocities,demonstrating excellent generalization ability.

OKO-SVM:Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews

Online incremental learning is one of the emerging research interests among the researchers in the recent years.The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews.This work has introduced an online incremental learning algorithm for classifying the train reviews.The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service.This work proposes the online kernel optimizationbased support vector machine(OKO-SVM)classifier for the sentiment classification of the train reviews.This paper is the extension of the previous work kernel optimizationbased support vector machine(KO-SVM).The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration.The simulation uses the standard train review and the movie review database for the classification.From the simulation results,it is evident that the proposed model has achieved a better performance with the values of 84.42%,93.86%,and 74.56%regarding the accuracy,sensitivity,and specificity while classifying the train review database.