Sentence examples for input uncertain from inspiring English sources

The input uncertain parameters are treated as interval variables, and the two representative convex-set models, hyper-rectangle model and hyper-ellipsoid model, are adopted to describe bounded uncertainties.

Algorithm 1 Deriving ( ⪯ C u, ⪯ C ∖ { 0 } u, ⪯ int C u ) -robust solutions to ( P ( U ) ) based on weighted sum scalarization: Input: Uncertain multi-objective problem P ( U ), solution sets Opt C = Opt C ∖ { 0 } = Opt int C = ∅.

Algorithm 2 Deriving a single accepted ( ⪯ C u, ⪯ C ∖ { 0 } u, ⪯ int C u ) -robust solution to ( P ( U ) ) based on weighted sum scalarization: Input: Uncertain vector-valued problem ( P ( U ) ). Step 1: Choose a nonempty set C ¯ ⊂ C ∗ ∖ { 0 }.

Due to this Lemma 2, from Algorithms 1 and 3, we can deduce the following algorithm for calculating ⪯ C a -robust solutions to ( P ( U ) ). Algorithm 7 Deriving ( ⪯ C a / ⪯ C ∖ { 0 } a / ⪯ int C a ) -robust solutions to ( P ( U ) ): Input: Uncertain multi-objective problem ( P ( U ) ), solution sets Opt C a = Opt C ∖ { 0 } a = Opt int C a = ∅.

This paper studies the problem of actuator-nonlinearities compensation in the multi-input uncertain neutral systems.

An example is presented to show its application to multi-input uncertain LTI systems.

This paper investigates the problem of designing a linear state feedback control to stabilize a new class of single-input uncertain linear dynamical systems.

Moreover, increase in the price of agricultural inputs, uncertain price of perishable agricultural produce, inadequate market infrastructure, distress sale of produce by small and marginal farmers are some of the problems that pose serious challenges to sustainability of farm sector in the state.

Step1 Representation of stochastic model inputs: For each uncertain input, corresponding srv is assigned and the input random variable is expressed in terms of the srv.

Negotiations that involve the use and interpretation of scientific information and assessment are often particularly difficult, especially when the scientific input is uncertain or contested.

Robust controllers for nonlinear systems with non Gaussian random inputs and uncertain parameters can be reliably designed using probabilistic methods.