linear function
Neural Networks A General Framework for Non Linear Function Approximation
... non-linear function approximation where the form of the mapping is governed by a number of adjustable ...regression function that we wish to ...
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On a Linear Control Problem under Interference with a Payoff Depending on the Modulus of a Linear Function and an Integral
... a linear control problem with an interference. The resulting linear problem is an example of a more general linear control problem in the presence of interference, in which the payoff consists of two ...
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Neonatal length as a linear function of adult length in Cetacea
... to birth, j,e" in the course of the specific phase, is directly proportional to adult length, By the use of the concept of composite lengths this relation assumes the symmetrical and ele[r] ...
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Estimation of Smoothing Constant of Minimum Variance Searching Optimal Parameters of Weight in the Case of Medical Operating equipment and supplies
... a linear function was applied to the sum total data of production and imports of medical operating equipment and supplies for three cases (An injection device and a puncture device, A sterilized hypodermic ...
13
PubMedCentral-PMC5031945.pdf
... a linear model or non-linear model with single monotonic non-linear function, in the ODS design of the CPP study which concentrates more resources on the tails of the outcome, we do not ...
14
Tracking the Maximum Power Point With Artificial Neural Network
... sigmoid function as an activation function in order to calculate the hidden layer output and a linear function to calculate the output ...
12
On the Optimization Problem of Stochastic Observations of Random Walks
... The linear function of the intensity as a cost of observations and the expected value of the quadratic form of errors of estimation as a cost of an error are reckoned in a loss ...
5
Realization of Neural Network for Pattern Detection using VHDL
... When we speak of intelligence it is actually acquired, learned from the past experiences. This intelligence, though a biological word, is realized based on the mathematical equations, giving rise to the science of ...
7
Admissibility of Linear Predictors of Finite Population Parameters under Reflected Normal Loss Function
... a linear function of characteristic values of a finite ...of linear predictors of an arbitrary linear function of characteristic values in a finite population under reflected normal ...
10
A List of Maximum Period NLFSRs
... Non-Linear Feedback Shift Registers (NLFSR) are a generalization of LFSRs in which a current state is a non-linear function of the previous state [21]. While the the- ory behind LFSRs is ...
9
A Discriminative Learning Model for Coordinate Conjunctions
... specific function for a single domain, we propose a general discriminative learning model in which the score function is a linear function of the features as- signed to vertices and edges in ...
10
Prediction Using Regression Analysis
... Simple Linear Regression Model (SLRM), Simple linear regression is similar to a linear regression model with an additional single explanatory ...simple linear regression model consists of a ...
5
Applying Quantum Optimization Algorithms for Linear Programming
... words, linear programming is a technique used to optimize a linear objective function, subject to linear equality and linear inequality ...a linear inequality. The objective ...
6
Knowledge-Based Kernel Approximation
... the function approximation with no prior knowledge was ...mean function value of the data used in the ten-fold cross validation exper- iments is ...the function approximation ...
15
On the Consistency of a Firm’s Value with a Lognormal Diffusion Process
... In this paper, we explicitly model a firm that performs intertemporal profit maximization. Our model assumes there is a futures market for the firm’s output. It specifies an internal production function for the ...
7
A New Analytical Model for Developing Fractional Flow Curve Using Production Data
... observed trends, for example, linear water cut versus oil recovery [3]. While these methods have been applied extensively, few have been found to be sufficiently robust. Moreover, only the relationship between ...
10
ENERGY EFFICIENT DISTRIBUTED IMAGE COMPRESSION USING JPEG2000 IN WIRELESS SENSOR NETWORKS (WSNS)
... A dataset was chosen for evaluation of the activation network. A simulator was specially developed for testing the activation function using an open source library fann (Fast Artifical Neural Network). The ...
5
Compensated convex transforms and geometric singularity extraction from semiconvex functions
... distance function and the squared distance ...semiconvex/semiconcave function is ...distance function dist 2 ( · , Ω c ) to the complement of a bounded open domain Ω ⊂ R n (called the medial axis [6] ...
25
Generalised linear models for flexible parametric modelling of the hazard function
... Standard one and two parameter models are available, including the exponential, Weibull, Gompertz, log-logistic and lognormal [5]. However, these models may not be sufficiently flexible to capture complex, time-varying ...
22
Generalized linear models for flexible parametric modeling of the hazard function
... hazard function allows for predictions of future ...response function, although this comes at the cost of needing to understand how and when to imple- ment these ...response function, these models ...
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