Application of an Artificial Neural Network and Multiple Nonlinear Regression to Estimate Container Ship Length Between Perpendiculars

Abstract

Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

[Retracted] Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition

“…Feature Extraction of Face Image. On the basis of face image imaging processing and edge contour detection, the recognition algorithm of the face image is optimized [12,13]. is paper presents a multipose face image recognition algorithm based on artificial neural network learning.…”

[Retracted] Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

“…With the continuous development of modern sensing technology, it enables us to grasp the speed-force curve relationship in the process of strength training, and also lays a technical foundation for the successful implementation of optimal power load strength training. Many training benefits of optimal power load strength training are inseparable from the arrangement and monitoring of “dose load,” how to accurately determine the optimal power load is the primary factor in strength training practice [ 11 ]. Usually, determining the optimal power load requires a maximum strength test and an output power test.…”

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

“…Even though each approach has its advantages, the study of intelligent control algorithms continually pushes experts to find theoretical and practical solutions to help the system run more consistently and securely, and to carry out many more tasks than were previously conceivable. Many academics are devoted to developing improved technology and artificial intelligence because of the exceptional benefits that may be possible [5][6][7][8][9][10]. Fuzzy [5], Hybrid Fuzzy [6], Fuzzy Adaptive [7], Neural Network [8], Genetic Algorithm (GA) [9], and Particle Swarm Optimisation (PSO) [10] are a few of the recommended advanced control approaches that have demonstrated their efficacy and stability.…”

“…Many academics are devoted to developing improved technology and artificial intelligence because of the exceptional benefits that may be possible [5][6][7][8][9][10]. Fuzzy [5], Hybrid Fuzzy [6], Fuzzy Adaptive [7], Neural Network [8], Genetic Algorithm (GA) [9], and Particle Swarm Optimisation (PSO) [10] are a few of the recommended advanced control approaches that have demonstrated their efficacy and stability. In addition, there has been a lack of investigation into utilising existing control theory to improve the performance and efficacy of controllers, particularly complex systems or systems controlled through networks.…”