scholarly journals Boolean Models Guide Intentionally Continuous Information and Computation Inside the Brain

2019 ◽  
Vol 12 (Issue 3) ◽  
pp. 90-98
Author(s):  
Germano Resconi
Keyword(s):  
Boolean Function ◽  
Back Propagation ◽  
Activation Function ◽  
Electrical Signal ◽  
Boolean Models ◽  
Input Signals ◽  
Long Time ◽  
Computer Logic ◽  
Logic Functions ◽  
Formal Neuron

In 1943 Machculloch and Pitts create the formal neuron where many input signals are linearly composed with different weights on the neuron soma. When the soma electrical signal goes over a specific threshold an output is produced. The main topic in this model is that the response is the same response as in a Boolean function used a lot for the digital computer. Logic functions can be simplified with the formal neuron. But there is the big problem for which not all logic functions, as XOR , cannot be designed in the formal neuron. After a long time the back propagation and many other neural models overcame the big problem in some cases but not in all cases creating a lot of uncertainty. The model proposed does not consider the formal neuron but the natural network controlled by a set of differential equations for neural channels that model the current and voltage on the neuron surface. The steady state of the probabilities is the activation state continuous function whose maximum and minimum are the values of the Boolean function associated with the activation time of spikes of the neuron. With this method the activation function can be designed when the Boolean functions are known. Moreover the neuron differential equation can be designed in order to realize the wanted Boolean function in the neuron itself. The activation function theory permits to compute the neural parameters in agreement with the intention.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Modeling of Stage-Discharge Using Back Propagation ANN, ANFIS, and WANN-based Computing Technique

2021 ◽  
Author(s):  
Ravi Shukla ◽  
Pravendra Kumar ◽  
Dinesh Kumar Vishwakarma ◽  
Rawshan Ali ◽  
Rohitashw Kumar ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Activation Function ◽  
Neural System ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Computing Technique ◽  
Artificial Neural

Abstract The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge. The daily data of 12 years (2007-2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

scholarly journals Linearization of multi-output logic functions by ordering of the autocorrelation values

2007 ◽  
Vol 20 (3) ◽  
pp. 479-498 ◽  
Author(s):  
Osnat Keren ◽  
Ilya Levin
Keyword(s):  
Computational Complexity ◽  
Boolean Function ◽  
Autocorrelation Function ◽  
Execution Time ◽  
Boolean Functions ◽  
Analytic Method ◽  
Implementation Cost ◽  
Initial Domain ◽  
Greedy Methods ◽  
Logic Functions

The paper deals with the problem of linear decomposition of a system of Boolean functions. A novel analytic method for linearization, by reordering the values of the autocorrelation function, is presented. The computational complexity of the linearization procedure is reduced by performing calculations directly on a subset of autocorrelation values rather than by manipulating the Boolean function in its initial domain. It is proved that unlike other greedy methods, the new technique does not increase the implementation cost. That is, it provides linearized functions with a complexity that is not greater than the complexity of the initial Boolean functions. Experimental results over standard benchmarks and random Boolean functions demonstrate the efficiency of the proposed procedure in terms of the complexity measure and the execution time.

Neural network-based hardware classifier using CORDIC algorithm

2020 ◽  
Vol 34 (15) ◽  
pp. 2050161
Author(s):  
Vipin Tiwari ◽  
Ashish Mishra
Keyword(s):  
Neural Network ◽  
Programming Languages ◽  
Back Propagation ◽  
Activation Function ◽  
Data Repository ◽  
Cordic Algorithm ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Benchmark Datasets ◽  
Error Back Propagation Algorithm

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers.

NEW METHOD OF NEURON DESIGN BASED ON DISCRETE Z-FUNCTION TO ADAPT THE CHANGE OF INTEGRATED VEHICLE STABILITY CONTROL ORDER

2009 ◽  
Vol 08 (03) ◽  
pp. 253-285 ◽  
Author(s):  
M. HARLY ◽  
I. N. SUTANTRA ◽  
H. P. MAURIDHI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Activation Function ◽  
Zero Order ◽  
Vehicle Stability ◽  
Adaptive Control System ◽  
Control Performance ◽  
Fixed Order ◽  
Hidden Layer

Fixed order neural networks (FONN), such as high order neural network (HONN), in which its architecture is developed from zero order of activation function and joint weight, regulates only the number of weight and their value. As a result, this network only produces a fixed order model or control level. These obstacles, which affect preceeding architectures, have been performing finite ability to adapt uncertainty character of real world plant, such as driving dynamics and its desired control performance. This paper introduces a new concept of neural network neuron. In this matter, exploiting discrete z-function builds new neuron activation. Instead of zero order joint weight matrices, the discrete z-function weight matrix will be provided to realize uncertainty or undetermined real word plant and desired adaptive control system that their order has probably been changing. Instead of using bias, an initial condition value is developed. Neural networks using new neurons is called Varied Order Neural Network (VONN). For optimization process, updating order, coefficient and initial value of node activation function uses GA; while updating joint weight, it applies both back propagation (combined LSE-gauss Newton) and NPSO. To estimate the number of hidden layer, constructive back propagation (CBP) was also applied. Thorough simulation was conducted to compare the control performance between FONN and MONN. In order to control, vehicle stability was equipped by electronics stability program (ESP), electronics four wheel steering (4-EWS), and active suspension (AS). 2000, 4000, 6000, 8000 data that are from TODS, a hidden layer, 3 input nodes, 3 output nodes were provided to train and test the network of both the uncertainty model and its adaptive control system. The result of simulation, therefore, shows that stability parameter such as yaw rate error, vehicle side slip error, and rolling angle error produces better performance control in the form of smaller performance index using FDNN than those using MONN.

scholarly journals Speeding Up Back-Propagation Neural Networks

10.28945/2931 ◽  
2005 ◽  
Author(s):  
Mohammed A. Otair ◽  
Walid A. Salameh
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Network Size ◽  
Learning Rate ◽  
Network Architectures ◽  
Local Minima ◽  
Set Size ◽  
Long Time ◽  
Multilayer Neural Networks ◽  
Optical Time

There are many successful applications of Backpropagation (BP) for training multilayer neural networks. However, it has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process. The proposed algorithm presented in this study used for training depends on a multilayer neural network with a very small learning rate, especially when using a large training set size. It can be applied in a generic manner for any network size that uses a backpropgation algorithm through an optical time (seen time). The paper describes the proposed algorithm, and how it can improve the performance of back-propagation (BP). The feasibility of proposed algorithm is shown through out number of experiments on different network architectures.

scholarly journals Electronic model of a dubois fuzzy integration neuron

2009 ◽  
Vol 7 (01) ◽  
Author(s):  
J.L. Pérez S. ◽  
A. Garcés M ◽  
F. Cabiedes C. ◽  
A. Miranda V.
Keyword(s):  
Membership Function ◽  
Integration Method ◽  
Threshold Value ◽  
Activation Function ◽  
Fuzzy Integral ◽  
Electronic Circuits ◽  
Electronic Model ◽  
Input Signals ◽  
Leaky Integrator ◽  
Electronic Neuron

In this work, we present a fuzzy electronic neuron that has a Dubois fuzzy integration method, an activation function with a fuzzy threshold, and a fuzzy response. We generated a fuzzy sum of the input signals and a shooting threshold value defined by means of a triangular or sinusoidal membership function. We present the electronic circuits, the oscilograms of the neuron responses, the value of the fuzzy integral, and we compare their behavior with those of a conventional leaky integrator neuron.

Comparison of Different Neural Network Training Algorithms for Wind Velocity Forecasting

2016 ◽  
Vol 819 ◽  
pp. 346-350
Author(s):  
Morteza Khalaji Assadi ◽  
Shervin Safaei
Keyword(s):  
Wind Speed ◽  
Wind Velocity ◽  
Input Data ◽  
Back Propagation ◽  
Activation Function ◽  
Gradient Algorithm ◽  
Training Algorithms ◽  
Use Of Data ◽  
Network Training ◽  
Quasi Newton

In this paper the wind speed is predicted by the use of data provided from the Mehrabad meteorological station located in Tehran, Iran, Collected between 2003 and 2008. A comprehensive analogy study is presented on Comparison of various Back Propagation neural networks methods in wind velocity forecasting. Four types of activation functions, namely, BFGS quasi-Newton, Bayesian regularized, Levenberg-Marquardt, and conjugate gradient algorithm, werestudied. The data was investigated by correlation coefficient and characterizing the amount of dependency between the wind speed and other input data. The meteorological parameters (pressure, direction, temperature and humidity) were used as input data, while the wind velocity is used as the output of the network.The results demonstrate that for the similar wind dataset, Bayesian Regularized algorithm can accurately predict compared with other method. In addition, choosing the type of activation function is dependent on the amount of input data, which should be acceptably large.

A Model with Back-Propagation Algorithm for the Estimation of Irreducible Water Saturation

2011 ◽  
Vol 361-363 ◽  
pp. 445-450 ◽  
Author(s):  
Ping Hua Ma ◽  
Hong Fu Fan ◽  
Ke Li
Keyword(s):  
Prediction Models ◽  
Water Saturation ◽  
Back Propagation ◽  
Superior Performance ◽  
Back Propagation Algorithm ◽  
Irreducible Water Saturation ◽  
Long Time ◽  
Multi Phase Flow ◽  
Conventional Models ◽  
Hidden Layer

As one of the most important reservoir parameters, irreducible water saturation, Swir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of Swir. In traditionally, Swir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of Swir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine Swir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed Swir prediction models.

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