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A nonmonotone scaled conjugate gradient algorithm for large-scale unconstrained optimization

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2018年第11期95卷 2212-2228页

作者： Ou, Yigui Zhou, Xin Hainan Univ Dept Appl Math Haikou 570228 Hainan Peoples R China

This paper proposes a nonmonotone scaled conjugate gradient algorithm for solving large-scale unconstrained optimization problems, which combines the idea of scaled memoryless Broyden-Fletcher-Goldfarb-Shanno preconditioned conjugate gradient method with the nonmonotone technique. An attractive property of the proposed method is that the search direction always provides sufficient descent step at each iteration. This property is independent of the line search used. Under appropriate assumptions, the method is proven to possess global convergence for nonconvex smooth functions, and R-linear convergence for strongly convex functions. Preliminary numerical results and related comparisons show the efficiency of the proposed method in practical computation.

关键词： Unconstrained optimization nonmonotone line search scaled conjugate gradient algorithm convergence analysis numerical comparisons

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Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network

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SOFT COMPUTING 2023年第10期27卷 6687-6702页

作者： Paul, Banibrata Karn, Bhaskar BIT Dept Comp Sci & Engn Ranchi Jharkhand India

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results;similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing.

关键词： Artificial neural network Cleveland datasets Cleveland Hungarian Statlog heart dataset Heart disease scaled conjugate gradient algorithm

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Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

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ENERGIES 2024年第9期17卷 2233页

作者： Vladov, Serhii Yakovliev, Ruslan Bulakh, Maryna Vysotska, Victoria Kharkiv Natl Univ Internal Affairs Kremenchuk Flight Coll 17-6 Peremohy St UA-39605 Kremenchuk Ukraine Rzeszow Univ Technol Fac Mech & Technol PL-37450 Stalowa Wola Poland Lviv Polytech Natl Univ Informat Syst & Networks Dept 12 Bandera St UA-79013 Lvov Ukraine Osnabruck Univ Inst Comp Sci 1 Friedrich Janssen St D-49076 Osnabruck Germany

The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines' optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.

关键词： neural network approximation helicopter turboshaft engines energy power efficiency training gas-generator rotor r.p.m. scaled conjugate gradient algorithm

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Hybrid ANN-Based Model for Lung Cancer Disease Prediction 3

Hybrid ANN-Based Model for Lung Cancer Disease Prediction

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Paul, Banibrata Mir, Tawseef Ahmad Murugan, Stalin Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350317022

Currently, lung cancer ranks as the world's leading cause of cancer-related fatalities in both men and women. The primary cause of lung cancer may be attributed to smoking. Although lung cancer can develop in any part of the lung, 90% to 95% of cases are believed to start in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles). This study's main goal is to identify lung cancer by employing artificial neural networks with scaled conjugate gradient back propagation and k-fold cross validation. We have utilized the Lung Cancer dataset through kaggle. Data from 1000 lung cancer patients, ages 14 to 73, is used to train the network. How many neurons are in the hidden layer affects how accurate the results are. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, yielding a minimum accuracy percentage of 83.1429% and a maximum accuracy percentage of 100%. © 2023 IEEE.

关键词： Artificial Neural Network Back propagation Lung Cancer Lung cancer dataset scaled conjugate gradient algorithm

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Utilizing AI and IoT technologies for identifying risk factors in sports

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HELIYON 2024年第11期10卷 e32477页

作者： Chen, Zhiling Dai, Xinghong Univ South China Sch Phys Educ Hengyang Hunan Peoples R China Hunan Univ Sch Phys Educ Changsha Hunan Peoples R China

A dynamic cooperation is poised to redefine the limits of athlete safety and performance optimization in the dynamic field of sports science. A new age in sports analysis is promised by the combination of artificial intelligence (AI) and the internet of things (IoT), one in which datadriven insights not only improve our comprehension of athletic performance but also aid to reduce hazards. This academic work explores the complex interactions between AI and IoT in the context of sports. The IoT and AI integration appear to be a strong mix that has the potential to redefine the standards for athlete safety and performance improvement. This study explores the complex interactions between AI and IoT in the field of sports, emphasizing their combined potential for identifying risk factors in a variety of fields. There is a chance to proactively solve sports-related difficulties by utilizing the data-driven capabilities of IoT and the analytical power of AI, opening the door for better informed tactics and decision-making. Through an exploration of this symbiotic relationship, this paper seeks to underline the transformative potential of these technologies in fostering a safer and more performance-oriented sports environment.

关键词： AI IoT Sports analytics Risk factor identification scaled conjugate gradient algorithm Athlete safety Performance optimization

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Learning algorithms for Quaternion-Valued Neural Networks

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NEURAL PROCESSING LETTERS 2018年第3期47卷 949-973页

作者： Popa, Calin-Adrian Polytech Univ Timisoara Dept Comp & Software Engn Blvd V Parvan 2 Timisoara 300223 Romania

This paper presents the deduction of the enhanced gradient descent, conjugate gradient, scaled conjugate gradient, quasi-Newton, and Levenberg-Marquardt methods for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of these algorithms in the real- and complex-valued cases led to the idea of extending them to the quaternion domain, also. Experiments done using the proposed training methods on time series prediction applications showed a significant performance improvement over the quaternion gradient descent algorithm.

关键词： Quaternion-valued neural networks Quickprop Resilient backpropagation Delta-bar-delta SuperSAB conjugate gradient algorithms scaled conjugate gradient algorithm Quasi-Newton algorithms Levenberg-Marquardt algorithm Time series prediction

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scaled conjugate gradient Learning for Quaternion-Valued Neural Networks

Scaled Conjugate Gradient Learning for Quaternion-Valued Neu...

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Popa, Calin-Adrian Polytech Univ Timisoara Dept Comp & Software Engn Blvd V Parvan 2 Timisoara 300223 Romania

ISBN: (纸本)9783319466750;9783319466743

This paper presents the deduction of the scaled conjugate gradient method for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of the scaled conjugate algorithm in the real- and complex-valued cases lead to the idea of extending it to the quaternion domain, also. Experiments done using the proposed training method on time series prediction applications showed a significant performance improvement over the quaternion gradient descent and quaternion conjugate gradient algorithms.

关键词： Quaternion-valued neural networks scaled conjugate gradient algorithm Time series prediction

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Neural-based prediction of structural failure of multistoried RC buildings

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STRUCTURAL ENGINEERING AND MECHANICS 2016年第3期58卷 459-473页

作者： Hore, Sirshendu Chatterjee, Sankhadeep Sarkar, Sarbartha Dey, Nilanjan Ashour, Amira S. Balas-Timar, Dana Balas, Valentina E. Hooghly Engineeing&Technol Coll Chinsurah Dept Comp Science&Engn Hyderabad Andhra Pradesh India Univ Calcutta Dept Comp Sci & Engn Kolkata India Hooghly Engn & Technol Coll Chinsurah Dept Civil Engn Hyderabad Andhra Pradesh India Techno India Coll Technol Dept Informat Technol Kolkata India Tanta Univ Fac Engn Dept Elect & Elect Commun Engn Cairo Egypt Aurel Vlaicu Univ Arad Fac Educ Sci Psychol & Social Sci Bucharest Romania Aurel Vlaicu Univ Arad Fac Engn Bucharest Romania

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

关键词： Reinforced Concrete (RC) structures structural failure Artificial Neural Network (ANN) Multilayer Perceptron Feed-Forward Network (MLP-FFN) scaled conjugate gradient algorithm cross-entropy

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SAGRAD: A Program for Neural Network Training with Simulated Annealing and the conjugate gradient Method

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JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 2015年第3期120卷 113-128页

作者： Bernal, Javier Torres-Jimenez, Jose NIST Gaithersburg MD 20899 USA CINVESTAV Tamaulipas Informat Technol Lab Victoria Tamaulipas Mexico

SAGRAD (Simulated Annealing gradient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Moller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Moller's algorithm;the (re) initialization of weights with simulated annealing required to (re) start Moller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum;and the use of simulated annealing when Moller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

关键词： batch learning neural networks for classification scaled conjugate gradient algorithm simulated annealing

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Short-Term Load Forecasting Based on a Improved Deep Belief Network

Short-Term Load Forecasting Based on a Improved Deep Belief ...

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6th International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)

作者： Zhang, Xiaoyu Wang, Rui Zhang, Tao Zha, Yabin Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467389044

Short-term load forecast plays a vital role in the electric power industry. In this paper, a deep belief network is proposed for short-term load forecasting. However, the Back-Propagation (BP) algorithm of deep belief network for fine tuning has some inherent drawbacks and limitations, such as slow convergence rate and easy to fall into local minimum. To overcome the drawbacks, several neural network weight optimizing methods are proposed and compared. Experimental results show that Levenberg-Marquardt (LM) algorithm has the highest accuracy.

关键词： short-term load forecast deep belief network scaled conjugate gradient algorithm one step secant algorithm Levenberg-Marquardt algorithm

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