scholarly journals Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 339
Author(s):  
Tom Kusznir ◽  
Jaroslaw Smoczek
Keyword(s):  
Genetic Programming ◽  
Prediction Model ◽  
Expert Knowledge ◽  
Lagrange Equation ◽  
Local Optimum ◽  
Dynamic Prediction ◽  
Prediction Horizon ◽  
Experimental Stand ◽  
Marquardt Algorithm ◽  
Predictor Model

This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method.

scholarly journals Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runwen Liu ◽  
Yunqiang Cai ◽  
He Cai ◽  
Yajia Lan ◽  
Lingwei Meng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pancreatic Fistula ◽  
Postoperative Pancreatic Fistula ◽  
Laparoscopic Pancreaticoduodenectomy ◽  
C Reactive Protein ◽  
Dynamic Prediction ◽  
Albumin Ratio ◽  
Drainage Fluid ◽  
Reactive Protein ◽  
Baseline Factors

Abstract Background With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio. Methods We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point. Results The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively. Conclusions The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

A dynamic prediction model for time‐to‐peak

2021 ◽  
Author(s):  
Mistaya Langridge ◽  
Ed McBean ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Prediction Model ◽  
Dynamic Prediction ◽  
Time To Peak

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

scholarly journals S136. A NOVEL APPROACH FOR DEVELOPING PREDICTION MODEL OF TRANSITION TO PSYCHOSIS: DYNAMIC PREDICTION USING JOINT MODELLING

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S378-S379 ◽  
Author(s):  
Hok Pan Yuen ◽  
Andrew Mackinnon ◽  
Jessica Hartmann ◽  
Paul Amminger ◽  
Connie Markulev ◽  
...  
Keyword(s):  
Prediction Model ◽  
Dynamic Prediction ◽  
Joint Modelling ◽  
Novel Approach

scholarly journals Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming

2020 ◽  
Vol 28 (1) ◽  
pp. 141-163 ◽  
Author(s):  
Masanori Suganuma ◽  
Masayuki Kobayashi ◽  
Shinichi Shirakawa ◽  
Tomoharu Nagao
Keyword(s):  
Genetic Programming ◽  
Expert Knowledge ◽  
Computational Cost ◽  
Computational Time ◽  
Network Architectures ◽  
Cartesian Genetic Programming ◽  
Deep Convolutional Neural Networks ◽  
High Performing ◽  
Network Training ◽  
Automatic Design Methods

The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures requires more expert knowledge and trial and error. In this article, we attempt to automatically construct high-performing CNN architectures for a given task. Our method uses Cartesian genetic programming (CGP) to encode the CNN architectures, adopting highly functional modules such as a convolutional block and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity, represented by the CGP, are optimized to maximize accuracy using the evolutionary algorithm. We also introduce simple techniques to accelerate the architecture search: rich initialization and early network training termination. We evaluated our method on the CIFAR-10 and CIFAR-100 datasets, achieving competitive performance with state-of-the-art models. Remarkably, our method can find competitive architectures with a reasonable computational cost compared to other automatic design methods that require considerably more computational time and machine resources.

scholarly journals Parameter identification for symbolic regression using nonlinear least squares

2019 ◽  
Vol 21 (3) ◽  
pp. 471-501 ◽  
Author(s):  
Michael Kommenda ◽  
Bogdan Burlacu ◽  
Gabriel Kronberger ◽  
Michael Affenzeller
Keyword(s):  
Genetic Programming ◽  
Local Search ◽  
Parameter Identification ◽  
Least Squares ◽  
Automatic Differentiation ◽  
Nonlinear Least Squares ◽  
Symbolic Regression ◽  
Machine Learning Algorithms ◽  
Benchmark Problems ◽  
Marquardt Algorithm

AbstractIn this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquardt algorithm for parameter optimization and calculate gradients via automatic differentiation. We provide examples where the parameter identification succeeds and fails and highlight its computational overhead. Using an extensive suite of symbolic regression benchmark problems we demonstrate the increased performance when incorporating nonlinear least squares within genetic programming. Our results are compared with recently published results obtained by several genetic programming variants and state of the art machine learning algorithms. Genetic programming with nonlinear least squares performs among the best on the defined benchmark suite and the local search can be easily integrated in different genetic programming algorithms as long as only differentiable functions are used within the models.

scholarly journals Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study

2020 ◽  
Vol 15 (9) ◽  
Author(s):  
Marieke Welten ◽  
Alet H. Wijga ◽  
Marleen Hamoen ◽  
Ulrike Gehring ◽  
Gerard H. Koppelman ◽  
...  
Keyword(s):  
Cohort Study ◽  
High Risk ◽  
Young Children ◽  
Prediction Model ◽  
Dynamic Prediction ◽  
Internal Validation
Sign in / Sign up

Export Citation Format

Share Document