Search results for "logistic function"
showing 10 items of 11 documents
Global trends in NDVI-derived parameters obtained from GIMMS data
2011
The Normalized Difference Vegetation Index (NDVI) has been proven to be useful to assess vegetation changes around the world, in spite of limitations such as sensitivity to cloud or snow contamination. In order to map vegetation changes at global scale, this study uses NDVI time series provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group, which were fitted annually to a double logistic function. This fitting procedure allowed for retrieval of NDVI-derived parameters which were tested for trends using Mann-Kendall statistics. These trends were validated by comparison at 73 ground control points documented as change hotspots. The obtained trends for NDVI-derived paramet…
Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms : An experimental analysis
2022
Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are use…
On the Kneser property for reaction–diffusion equations in some unbounded domains with an -valued non-autonomous forcing term
2012
Abstract In this paper, we prove the Kneser property for a reaction–diffusion equation on an unbounded domain satisfying the Poincare inequality with an external force taking values in the space H − 1 . Using this property of solutions we check also the connectedness of the associated global pullback attractor. We study also similar properties for systems of reaction–diffusion equations in which the domain is the whole R N . Finally, the results are applied to a generalized logistic equation.
Logistic Growth Described by Birth-Death and Diffusion Processes
2019
We consider the logistic growth model and analyze its relevant properties, such as the limits, the monotony, the concavity, the inflection point, the maximum specific growth rate, the lag time, and the threshold crossing time problem. We also perform a comparison with other growth models, such as the Gompertz, Korf, and modified Korf models. Moreover, we focus on some stochastic counterparts of the logistic model. First, we study a time-inhomogeneous linear birth-death process whose conditional mean satisfies an equation of the same form of the logistic one. We also find a sufficient and necessary condition in order to have a logistic mean even in the presence of an absorbing endpoint. Then…
On the population model with a sine function
2006
In the interval [0,1] function sr(x) = r sin πx behaves similar to logistic function h μ (x) = μx(1‐ x). We prove that for every r > there exists subset ? ⊂ [0,1] such that sr : ? → ? is a chaotic function. Since the logistic function is chaotic in another subset of [0,1] but both functions have similar graphs in [0,1] we conclude that it can lead to errors in practice. First Published Online: 14 Oct 2010
Advantages of fitting contrast curves using logistic function: a technical note.
2013
Objective The aim of this article is to demonstrate how the contrast properties of an imaging system can be ideally fitted with the use of stripe patterns and the logistic function. Study Design Stripe patterns with defined amounts of line pairs (lp/mm) per mm (10-20 lp/mm) were recorded with the use of digital photostimulable storage phosphor. Scan data and normalized image data were analyzed with the use of ImageJ and MatLab to calculate different contrast curves. Results For original scan data, the goodness of fit was 0.0000019 (sum of squared error [SSE]). The R-square was 0.9998. For normalized data the goodness of fit was 0.0007 (SSE) and the R-square 0.998. An amount of 50% contrast …
Global land surface phenology trends from GIMMS database
2009
A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter. A principal component analysis on the resulting time series indicates that the first components explain 36, 53 and 37% of the variance for the start, end and length of growing season, respectively, and shows generally good spatial homogeneity. Mann-Kendall trend tests have been carried out, and trends were estimated by linear regression. Maps of these trends show a global advance in spring dates of 0.38 days per year, a global delay in aut…
Mould germination: data treatment and modelling.
2006
Abstract The objectives of this study were i/ to examine germination data sets over a range of environmental conditions (water activity, temperature) for eight food spoilage moulds, ii/ to compare the ability of the Gompertz equation and logistic function to fit the experimental plots, iii/ to simulate germination by assessing various distributions of the latent period for germination amongst a population of spores. Data sets (percentage germination, P (%), versus time, t) of Aspergillus carbonarius, Aspergillus ochraceus, Fusarium verticillioides, Fusarium proliferatum, Gibberella zeae, Mucor racemosus, Penicillium chrysogenum and Penicillium verrucosum were analysed. No correlation, or re…
On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines
2020
Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of classifications, e.g. to facilitate risk management. In this paper, we propose a novel scheme for measuring TM confidence based on the logistic function, calculated from the propositional logic patterns that match the input. We then use this scheme to trade off precision against recall, producing area under receiver operating characteristic curves (AUC) for TMs. Empirically, using four real-world datasets, we show that AUC is a more sensitive meas…
Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions
2021
Abstract We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of bot…