The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

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The parameter estimation of unstable aircraft using extreme learning machine method is presented. In the past, conventional methods such as output error method, filter error method, equation error method and nonconventional method such as artificial neural-network based methods have been used for aircraft's aerodynamic parameter estimation. Nowadays, a trend of finding an accurate nonlinear function approximation is required to represent the aircraft's equations-of-motion. Such type of nonlinear function approximation is usually achieved using artificial neural-network which is trained with the aircraft input-output flight data using a training algorithm. The accuracy of estimated parameters, which is achieved using the trained network, is highly dependent on the generalisation capability of the network which can be improved using extreme learning machine based network in contrast to artificial neural-network. To estimate the unstable aircraft parameters from the simulated flight data, Gauss-Newton based optimisation method has been used with a predefined aerodynamic model using the trained network. Further, the confidence of the estimated parameters has been shown in comparison to that of the standard parameter estimation methods in terms of the Cramer-Rao bounds.

The relationship between length and weight of three fish species Johnius coitor (Family Sciaenidae), Osteobrama cotio (Family Cyprinidae) and Gonialosa manmina (Family Clupeidae) unknown to Fishbase were studied from the Allahabad waters of the river Ganga in India. Fishes were captured bi‐monthly from March 2017 to June 2018 using gill nets and cast nets of various mesh sizes. The b values determined from length‐weight relationships (LWRs) were 3.246, 3.373 and 3.024 for J. coitor, O. cotio and G. manmina, respectively.

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