A hyperbolastic type-I diffusion process: Parameter estimation by means of the firefly algorithm.

Biosystems

Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, s/n, Campus de Fuentenueva, Universidad de Granada, 18071 Granada, Spain. Electronic address:

Published: January 2018

A stochastic diffusion process, whose mean function is a hyperbolastic curve of type I, is presented. The main characteristics of the process are studied and the problem of maximum likelihood estimation for the parameters of the process is considered. To this end, the firefly metaheuristic optimization algorithm is applied after bounding the parametric space by a stagewise procedure. Some examples based on simulated sample paths and real data illustrate this development.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biosystems.2017.11.001DOI Listing

Publication Analysis

Top Keywords

diffusion process
8
hyperbolastic type-i
4
type-i diffusion
4
process
4
process parameter
4
parameter estimation
4
estimation firefly
4
firefly algorithm
4
algorithm stochastic
4
stochastic diffusion
4

Similar Publications

Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.

View Article and Find Full Text PDF

Photon emission may be continuously produced from mechanical work through self-recoverable mechanoluminescence (ML). Significant progress has been made in high-performance ML materials in the past decades, but the rate-dependent ML kinetics remains poorly understood. Here, we have conducted systematic studies on the self-recoverable ML of Mn-doped SrZnOS (SrZnOS: Mn) under rapid compression up to ~10 GPa.

View Article and Find Full Text PDF

The transport, distribution, and budget of anthropogenic I in the Bohai and North Yellow Seas, China.

J Hazard Mater

January 2025

State Key Laboratory of Loess and Quaternary Geology, Shaanxi Key Laboratory of Accelerator Mass Spectrometry Technology and Application, Xi'an AMS Center, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China. Electronic address:

The potential release of radionuclides threatens marine ecosystems with the rapid development of coastal nuclear power plants in China. However, transport, dispersion, and final budget of anthropogenic radionuclides remain unclear, especially in the Bohai and North Yellow Seas, which are semi-enclosed marginal seas with poor water exchange. This study analyzed anthropogenic I concentration (a typical product of nuclear power plant operations) in seawater samples from this area.

View Article and Find Full Text PDF

Convergent-Diffusion Denoising Model for multi-scenario CT Image Reconstruction.

Comput Med Imaging Graph

January 2025

The Department of Computer and Data Science, Case Western Reserve University, Cleveland, OH, USA; The Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

A generic and versatile CT Image Reconstruction (CTIR) scheme can efficiently mitigate imaging noise resulting from inherent physical limitations, substantially bolstering the dependability of CT imaging diagnostics across a wider spectrum of patient cases. Current CTIR techniques often concentrate on distinct areas such as Low-Dose CT denoising (LDCTD), Sparse-View CT reconstruction (SVCTR), and Metal Artifact Reduction (MAR). Nevertheless, due to the intricate nature of multi-scenario CTIR, these techniques frequently narrow their focus to specific tasks, resulting in limited generalization capabilities for diverse scenarios.

View Article and Find Full Text PDF

Finite-time H output synchronization for DCRDNNs with multiple delayed and adaptive output couplings.

Neural Netw

January 2025

School of Artificial Intelligence and Automation, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address:

This work concentrates on solving the finite-time H output synchronization (FTHOS) issue of directed coupled reaction-diffusion neural networks (DCRDNNs) with multiple delayed and adaptive output couplings in the presence of external disturbances. Based on the output information, an adaptive law to adjust output coupling weights and a controller are respectively developed to ensure that the DCRDNNs achieve FTHOS. Then, in the special case of no external disturbances, a corollary on the finite-time output synchronization (FTOS) of the DCRDNNs with multiple delayed and adaptive output couplings is provided.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!