Sequential learning artificial fuzzy neural networks (SLAFNN) with single hidden layer

Rajasekaran, S.; Suresh, D.; Pai, G. A. Vijayalakshmi

doi:10.1016/s0925-2312(01)00609-9

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The normalized data enhances NN performance and improves the speed of algorithm convergence. In this paper, the widely used fitting min-max method of data normalization was used and is illustrated in equation (1) (Rajasekaran and Pai, 2003; Oreški and Andročec, 2020). The developed code in Visual C#.net conducted this calculation: where λ 1 and λ 2 are optional constant values based on statistical characteristic (in present work λ 1 = 0.2 and λ 2 = 0.8 were assumed), X i is the input variable, X min is the minimum value, X max is the maximum value and finally X n is the normalized data.…”

Section: Methodsmentioning

confidence: 99%

“…Feedforward NN is one of the simplest types of NNs that are used for classification, regression, and prediction. In Feedforward NN, the input data flows forward from the input to the output layer (Rajasekaran and Pai, 2003).…”

Section: Ga-ann Architecturementioning

confidence: 99%

“…This causes discretization of the search space even though the function is a continuous one. Therefore, a discrete function can be handled without additional cost (Rajasekaran and Pai, 2003;Oreški and Androc ˇec, 2020;Abdolrasol et al, 2021;Kotyrba et al, 2022).…”

Section: Ga-ann Architecturementioning

confidence: 99%

Section: Ga-ann Architecturementioning

confidence: 99%

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Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

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Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

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Section: Methodsmentioning

confidence: 99%

Section: Ga-ann Architecturementioning

confidence: 99%

Section: Ga-ann Architecturementioning

confidence: 99%

Section: Ga-ann Architecturementioning

confidence: 99%

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Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

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“…The unstructured and structured data generated in the healthcare industry are increasing daily. To handle this massive amount of data and extract the pattern or decision from the data, many deep supers wised, and deep unsupervised learning algorithms were developed (Rajasekaran & Pai, 2017). The major problem faced by the health care industry is developing a DNN model that will take a tremendous amount of training time and give less accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi‐disease classification model using deep neural network and Strassen's rectilinear fine‐tune bouncing training algorithm

Sarveshwaran,

Karthikeyan,

Premalatha

2023

Expert Systems

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A deep neural network (DNN) is being used in the healthcare industry to improve care delivery at a lower cost and in less time. A DNN is well‐known for its diagnostic applications. However, it is also increasingly being utilized to guide healthcare management decisions. DNNs have been applied to real‐life disease classification problems. The performances of DNN are improved by reducing the training time, performing fast classification, and improving the parameters such as accuracy and error rate, and so forth. We have proposed a hybrid DNN named Strassen's rectilinear fine‐tune bouncing training (SRFBT) Algorithm by combining Strassen's theorem and bouncing training algorithm. The simulation result shows that the SRFBT algorithm outperforms both the DNN, support vector machine, radial basis function network and deep belief network algorithms in terms of network training, testing time and accuracy on various UCI machine learning healthcare datasets. The SRFBT has improved performance with a minimum average training time of 43.6249 and a maximum accuracy of 96.86.

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Tidal level forecasting using functional and sequential learning neural networks

Rajasekaran

Thiruvenkatasamy

Lee

2006

Applied Mathematical Modelling

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Sequential learning artificial fuzzy neural networks (SLAFNN) with single hidden layer

Cited by 9 publications

References 9 publications

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Multi‐disease classification model using deep neural network and Strassen's rectilinear fine‐tune bouncing training algorithm

Tidal level forecasting using functional and sequential learning neural networks

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