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Artificial Neural Networks (ANNs) and their Application in Soil and Water Resources Engineering
Published in Dinesh C. S. Bisht, Mangey Ram, Recent Advances in Time Series Forecasting, 2021
M. Mohan Raju, Dinesh C. S. Bisht, A. Naresh, Harish Gupta, M. Gopal Naik
Model development was executed by considering groundwater recharge and groundwater discharge in the current year as input, i.e., at timestep t for an output of water table elevation in the current year at timestep t in the design of model 1. Subsequently, in model 2, an additional input of water table elevation of the previous year post monsoon at timestep t-1 was considered along with two other inputs considered in the model 1 as inputs. Further, in model 3 previous year post monsoon recharge was considered as an input, i.e., at timestep t-1 in addition to current year recharge and discharge, and so on as followed in Table 12.7. Likewise five models were designed for fuzzy logic and ten models for ANN (five with one hidden layer and five with two hidden layers). Fuzzy if-then rules imposed among the variables of the rule-based fuzzy systems i.e. “if antecedent proposition then consequent proposition”.
Technology of Intelligent Systems
Published in James A. Momoh, Mohamed E. El-Hawary, Electric Systems, Dynamics, and Stability with Artificial Intelligence Applications, 2018
James A. Momoh, Mohamed E. El-Hawary
A fuzzy model consists of a series of conditional and unconditional fuzzy propositions. A proposition or statement establishes a relationship between a value in the underlying domain and a fuzzy space. Proposition is one that is qualified as an “if” statement. The proposition following the if term if, is the antecedent or predicate and is an arbitrary fuzzy proposition. The proposition following the then term is the consequent and is also any arbitrary fuzzy proposition IfwisZthenxisY
Bridge Management Objectives and Methodologies
Published in J.E. Harding, G.E.R. Parke, M.J. Ryall, Bridge Management 3, 2014
The concept of bridge enclosure was stimulated by the issues described above. Initially the main focus was on extending maintenance intervals for painted steelwork. The UK Transport Research Laboratory have carried out a series of trials [3][4] since 1980 of providing an enclosure envelope around bridges to prevent airborne contaminants from being deposited on the steelwork. This research work has demonstrated that the corrosion rate inside an enclosure is reduced by 95% from that of unenclosed steel. The elimination of chlorides and sulphur from the environment inside an enclosure has been shown to make the use of unpainted steel a practical proposition, with consequent cost saving, safety and environmental benefits. Condensation within the enclosure was not found to present a corrosion problem provided the surfaces onto which it might fall and collect, the enclosure itself, are not susceptible to corrosion.
Logarithmic control, trajectory tracking, and formation for nonholonomic vehicles on Lie group SE(2)
Published in International Journal of Control, 2019
The remaining task for the trajectory tracking is to select suitable ω01 and vx01 such that stabilises the relative configuration g01 at the identity. Although there is no requirement for the relative velocity to satisfy the nonholonomic constraint, in order to use the stabilisation result in Proposition 3.1, we also need that as g01 → I. For this purpose, let and define the (auxiliary) adjoint configuration then where and one can check that Differentiating (29) as where leads to Then by comparing (5) and (33), we have Selecting with a positive value kθ provides that or and consequently , and Thus, the other control term in (31) or (33) can be written as Using these observations, the problem of the trajectory tracking can be reduced to the problem of set-point stabilisation for the following nonholonomic relative system with inputs and . We summarise these results and the consequent control law for the trajectory tracking in the following proposition.
Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality
Published in Applied Artificial Intelligence, 2018
E.I Papageorgiou, K Aggelopoulou, T.A Gemtos, G.D Nanos
where A1, A2,…, Ak and B are fuzzy sets of the variables X1, X2,…, Xk and Y. This process is known as monotonic selection in which the right part of the rule (X1 is A1 and …) is called “antecedent proposition” and the left side (Y is B) is called “consequent proposition.”
Cohomological equations for linear involutions
Published in Dynamical Systems, 2021
Erwan Lanneau, Stefano Marmi, Alexandra Skripchenko
This can be done by induction on D and consequent application of the Proposition 4.1. The rest follows from Borel-Cantelli argument and the positiveness of the largest Lyapunov exponent of the Teichmüller flow. The details can be found in [15]. We now address a proof of Proposition 4.1.