A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study
2022
Sensors
Preserved Fulltext
Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive ...
In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. ...
To alleviate the problems above, this article proposes a hybrid model combining mechanism analysis and self-adaptive RBF neural network for soft sensor modeling in energy production process. ...
doi:10.3390/s22041333
pmid:35214236
pmcid:PMC8963067
fatcat:qfzxalp7njaedg6iltnan3rjq4
Modern Soft-Sensing Modeling Methods for Fermentation Processes
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent ...
The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic ...
Comprehensive analysis of soft sensor models based on genetic algorithms (for the list of abbreviations see Table A1 ). ...
doi:10.3390/s20061771
pmid:32210053
fatcat:cicbldmt7jh3jcsomemjqbhrvi
Advances in neural information processing systems 9: Proceedings of the 1996 conference
1998
Computers and Mathematics with Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Advances in Neural Information ...
Algorithms and architecture. Genetic algorithms and explicit search statistics (Shumeet Baluja). Consistent classification, firm and soft (Yoram Baram) . ...
One-unit learning rules for independent component analysns (Aapo Hyv~h-inen and Erkki Oja). Recursive algorithms for approximating probabilities in graphical models (Tommi S. Jaakkola and Michael I. ...
doi:10.1016/s0898-1221(98)90499-0
fatcat:cwqgiuxzkrfqppnri7kzvpy4um
Soft Computing Techniques for Various Image Processing Applications: A Survey
2020
Journal Electrical and Electronic Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper represents a survey on various soft computing methods'-fuzzy logic, neural network, neuro-fuzzy systems, genetic algorithm, evolutionary computing, support vector machine etc. ...
Soft computing techniques have found numerous applications in various domains of image processing and computer vision. ...
Conclusion In this paper, we have attempted to compile the available soft computing tools and algorithms for various image processing applications. ...
doi:10.11648/j.jeee.20200803.11
fatcat:ko47gevpuvavjnptb2j6xtltje
Soft Computing Approaches to Fault Diagnosis for Dynamic Systems
2001
European Journal of Control
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. ...
In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. ...
Yu et al (1999) investigated semiindependent neural model, based on an RBF network, to generate enhanced residuals for diagnosing sensor faults in a reactor. ...
doi:10.3166/ejc.7.248-286
fatcat:v6hesxhjwbbtjfde6zci6qxv34
Review of Soft Sensors in Anaerobic Digestion Process
2021
Processes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Subsequently, the development history of the traditional soft sensor is systematically reviewed, the latest development of soft sensors was detailed, and the obstacles of the soft sensor in the industrial ...
Finally, the future development trend of deep learning in soft sensors is deeply discussed, and future research directions are provided. ...
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/pr9081434
fatcat:nyzp6rzvkjegbj7atyhxvc7nzu
Development of soft computing and applications in agricultural and biological engineering
2010
Computers and Electronics in Agriculture
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines ...
The future of development and application of soft computing in agricultural and biological engineering is discussed. Published by Elsevier B. ...
Introduction Soft computing is a set of computing techniques, such as Fuzzy Logic (FL), Artificial Neural Networks (ANNs), and Genetic Algorithms (GAs). ...
doi:10.1016/j.compag.2010.01.001
fatcat:quszrg4vuzgcvkh2uglutnujdm
Advanced neural network systems for solving complex real problems
2021
Neural Processing Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines and kernel methods. Fuzzy logic. Evolutionary and genetic algorithms. B Ignacio Rojas ...
latest discoveries and realizations in the foundations, theory, models and applications of systems inspired on nature, using computational intelligence methodologies, as well as in emerging areas related ...
Cognitive and neural functionalities in nanophotonic applications A hands-on tutorial on Transfer Learning for Deep Neural Networks was carried out during IWANN 2019. ...
doi:10.1007/s11063-021-10542-6
fatcat:64yftwndvvd3hf2gn75pr2oklm
Radial basis function neural networks: a topical state-of-the-art survey
2016
Open Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools. ...
The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. ...
A few algorithms aspiring to determine all the RBF training parameters in one step have also been proposed in the literature. In [65] , a hierarchical Bayesian model is introduced for training RBFs. ...
doi:10.1515/comp-2016-0005
fatcat:wm2ik77fi5ca7hbq66ssrnssr4
Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm
2015
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
A comparative analysis with two successful methods based on RBF networks has been included. ...
We use a hierarchical genetic algorithm (HGA) to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares ...
Acknowledgments The authors acknowledge the support of CONACYT Mexico, DAIP of the University of Guanajuato, the Information Technology Laboratory of CINVESTAV, Tamaulipas, and ITESI, Mexico. ...
doi:10.1155/2015/731207
fatcat:av2hkkg4dzekpkjuylnfnnzyny
Insect Detection in Rice Crop using Google Code Lab
2021
Turkish Journal of Computer and Mathematics Education
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The result shows a Many-layer Preceptor and Nonlinear Activation Feature comparison, as well as a percentage of accuracy contrast between MLP and RBF. MLP and RBF are neural network algorithms. ...
Clearly, the Neural Network classifier has a better presentation and precision. ...
The classification of leaves began in the early twentieth century [13] .Support Vector Machine (SVM), neural network, conventional manifold regression, k-Nearest Neighbor, and Genetic Algorithm are some ...
doi:10.17762/turcomat.v12i2.1977
fatcat:uc2alfvxzbbihp4svs7udktak4
The Review of Soft Computing Applications in Humanitarian Demining Robots Design
2016
Indian Journal of Science and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Using different methods such as Neural Network, Fuzzy Logic, Fuzzy Neural Network or other soft computing methods have been able to render the behaviors of these robots more intelligent, more precise, ...
Background/Objectives: This paper represents a review of the types of demining robots and application of different soft computing methods in improving their performance. ...
Multi-Layer Perceptron Neural Network Distinguishing mine type is another one of the applications of neural networks in demining robots. ...
doi:10.17485/ijst/2016/v9i4/55595
fatcat:hehn3iua2rgoddc6wtumflehgq
SURVEY OF INTELLIGENT CONTROL ALGORITHMS FOR HUMANOID ROBOTS
2005
IFAC Proceedings Volumes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic ...
algorithms) in the area of humanoid robotic systems. ...
Kitamura 1988 proposed a walking controller based on Hopfield neural network in combination with an inverted pendulum dynamic model. ...
doi:10.3182/20050703-6-cz-1902.01276
fatcat:wrzes2vitbf4rkkfn3c6evxuxe
Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs
2012
2012 IEEE Congress on Evolutionary Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm. ...
The population evolves on a bidimensional grid and is implicitly organized in geographical clusters that present a form of structural similarity between individuals. ...
, Nonlinear System Identification Based on SVR with Quasilinear Kernel 50, Xichun Yuan, Honggui Han and Junfei Qiao, The Sludge Volume Index Soft Sensor Model Based on PCA-ElmanNN 60, Senzhang Wang, Zhoujun ...
doi:10.1109/cec.2012.6256590
dblp:conf/cec/RotaruB12
fatcat:4ly3nrktw5habc6lf5err7d5py
Computational intelligence for industrial and environmental applications
2016
2016 IEEE 8th International Conference on Intelligent Systems (IS)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. ...
In this context, computational intelligence plays an important role in performing of complex nonlinear computations by creating models from the training data. ...
Ministry of Research within the project "GenData 2020" (2010RTFWBH). ...
doi:10.1109/is.2016.7737423
dblp:conf/is/LabatiGMPSS16
fatcat:ot5g5p7z5jfoxddtrm4xtyg7c4
« Previous
Showing results 1 — 15 out of 406 results