scholarly journals Adaptive Activation Function Generation for Artificial Neural Networks through Fuzzy Inference with Application in Grooming Text Categorisation

2019 ◽  
Author(s):  
Zheming Zuo ◽  
Jie Li ◽  
Bo Wei ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Inference ◽  
Activation Function ◽  
Function Generation ◽  
Artificial Neural

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

Parallel Hardware for Artificial Neural Networks Using Fixed Floating Point Representation

2011 ◽  
pp. 295-308
Author(s):  
Nadia Nedjah ◽  
Rodrigo Martins da Silva ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Activation Function ◽  
Floating Point ◽  
Weighted Sum ◽  
Design Environment ◽  
Silicon Area ◽  
Point Representation ◽  
Artificial Neural ◽  
Mathematical Computation

Artificial Neural Networks (ANNs) is a well known bio-inspired model that simulates human brain capabilities such as learning and generalization. ANNs consist of a number of interconnected processing units, wherein each unit performs a weighted sum followed by the evaluation of a given activation function. The involved computation has a tremendous impact on the implementation efficiency. Existing hardware implementations of ANNs attempt to speed up the computational process. However, these implementations require a huge silicon area that makes it almost impossible to fit within the resources available on a state-of-the-art FPGAs. In this chapter, a hardware architecture for ANNs that takes advantage of the dedicated adder blocks, commonly called MACs, to compute both the weighted sum and the activation function is devised. The proposed architecture requires a reduced silicon area considering the fact that the MACs come for free as these are FPGA’s built-in cores. Our system uses integer (fixed point) mathematics and operates with fractions to represent real numbers. Hence, floating point representation is not employed and any mathematical computation of the ANN hardware is based on combinational circuitry (performing only sums and multiplications). The hardware is fast because it is massively parallel. Besides, the proposed architecture can adjust itself on-the-fly to the user-defined configuration of the neural network, i.e., the number of layers and neurons per layer of the ANN can be settled with no extra hardware changes. This is a very nice characteristic in robot-like systems considering the possibility of the same hardware may be exploited in different tasks. The hardware also requires another system (a software) that controls the sequence of the hardware computation and provides inputs, weights and biases for the ANN in hardware. Thus, a co-design environment is necessary.

scholarly journals Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Systems ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Fungal Population ◽  
Support Tool ◽  
Artificial Neural ◽  
Hidden Layer ◽  
And Storage

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

2013 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Golam Kabir ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Business Environment ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Artificial Neural

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.

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