Modeling and Optimizing Boiler Design using Neural Network and Firefly Algorithm

2018 ◽  
Vol 27 (3) ◽  
pp. 393-412 ◽  
Author(s):  
Sangram Bhagwanrao Savargave ◽  
Madhukar Jagannath Lengare
Keyword(s):  
Neural Network ◽  
Firefly Algorithm ◽  
Modeling Technique ◽  
Type I ◽  
Boiler Plant ◽  
Statistical Measures ◽  
Emissions Modeling ◽  
The Neural Network ◽  
Boiler Design ◽  
Non Linear System

Abstract The significance of researches in modeling of boiler design and its optimization is high for saving energy and minimizing emissions. Modeling the boiler plant with all demands is rather challenging. A lot of techniques are reported in the literature for enhancing the boiler efficiency. The neural network scheme has been proved for the boiler design, and it provides a framework for the non-linear system models. In this paper, a hybrid of artificial neural network and firefly algorithm is proposed. The proposed modeling technique is simulated in MATLAB, and the experimentation is carried out extensively. The performance of the proposed modeling technique is demonstrated using type I and II error functions, followed by performing higher statistical measures such as error deviation and correlation analysis. Comparative analysis is made to substantiate the superiority of the proposed modeling technique.

scholarly journals Sensory Regulation of Network Components Underlying Ciliary Locomotion in Hermissenda

2008 ◽  
Vol 100 (5) ◽  
pp. 2496-2506 ◽  
Author(s):  
Terry Crow ◽  
Lian-Ming Tian
Keyword(s):  
Neural Network ◽  
Spike Activity ◽  
Electrical Coupling ◽  
Rhythmic Activity ◽  
Burst Activity ◽  
Type I ◽  
Sensory Regulation ◽  
The Neural Network ◽  
Efferent Neurons ◽  
Ciliary Locomotion

Ciliary locomotion in the nudibranch mollusk Hermissenda is modulated by the visual and graviceptive systems. Components of the neural network mediating ciliary locomotion have been identified including aggregates of polysensory interneurons that receive monosynaptic input from identified photoreceptors and efferent neurons that activate cilia. Illumination produces an inhibition of type Ii (off-cell) spike activity, excitation of type Ie (on-cell) spike activity, decreased spike activity in type IIIi inhibitory interneurons, and increased spike activity of ciliary efferent neurons. Here we show that pairs of type Ii interneurons and pairs of type Ie interneurons are electrically coupled. Neither electrical coupling or synaptic connections were observed between Ie and Ii interneurons. Coupling is effective in synchronizing dark-adapted spontaneous firing between pairs of Ie and pairs of Ii interneurons. Out-of-phase burst activity, occasionally observed in dark-adapted and light-adapted pairs of Ie and Ii interneurons, suggests that they receive synaptic input from a common presynaptic source or sources. Rhythmic activity is typically not a characteristic of dark-adapted, light-adapted, or light-evoked firing of type I interneurons. However, burst activity in Ie and Ii interneurons may be elicited by electrical stimulation of pedal nerves or generated at the offset of light. Our results indicate that type I interneurons can support the generation of both rhythmic activity and changes in tonic firing depending on sensory input. This suggests that the neural network supporting ciliary locomotion may be multifunctional. However, consistent with the nonmuscular and nonrhythmic characteristics of visually modulated ciliary locomotion, type I interneurons exhibit changes in tonic activity evoked by illumination.

scholarly journals Application of Firefly Algorithm based Neural Network Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2659-2662
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Hidden Layer

The Firefly Algorithm is comparison of new optimize procedure based on PSO as tautness. The paper presents the competence and forcefulness of the Firefly algorithm as the optimize concept for a proportional–integral–derivative organizer under various loading conditions. The proposed PID controller is attempt to designed and implemented to frequency-control of a two area interconnected systems. The hidden layer formation is not personalized, as the interest lies only on the reckoning of the weights of the system. In sequence to obtain a practicable report, the weights of the neural network are computational or optimized by minimizing function cost or error. A Firefly Algorithm is an efficient but uncomplicated meta-heuristic optimization technique inspired by expected motion of fireflies towards more light, is used for the preparation of neural network. The simulation report view that the calculation competence of training progression using Firefly Optimization performance with Load frequency control. A study of the output report of the system PID controller and FA based neural network controllers are made for 1% change in load in area 1 and it is found that the proposed controllers ensures a better steady state response of the systems

Study of Electricity Powered Four-Wheel Steering System Based on Fuzzy Neural Network

2014 ◽  
Vol 716-717 ◽  
pp. 1494-1499
Author(s):  
Wei Dong Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Steering System ◽  
Front Wheel ◽  
Slip Angle ◽  
Self Healing ◽  
Side Slip Angle ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Non Linear System

By the analysis of the operational principle of electricity powered four-wheel steering system, a new system based on the fuzzy neural network. Since this is a complex multivariate and non-linear system, by making use of the characteristics of fuzzy control and the neural network, a fuzzy neural network can be established. The speed of car and front-wheel steering angle being the input and steering model being the output, the side-slip angle of the in the process of steering can be control to zero. At last, by emulating this system with the software Matlab/Simulink, it shows that self-healing control technology can effectively control the side-slip angle and improve the motility and stability of a car.

Modelling methane-air turbulent diffusion flame in a gas turbine combustor with artifical neural network

2009 ◽  
Vol 113 (1146) ◽  
pp. 541-547
Author(s):  
N. S. Mehdizadeh ◽  
P. Sinaei
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Network Architecture ◽  
Turbulent Flame ◽  
Ann Model ◽  
Turbulent Diffusion Flame ◽  
Air Jet ◽  
The Neural Network ◽  
Non Linear ◽  
Non Linear System

Abstract The present paper reports a way of using an artificial neural network (ANN) for modelling methane-air jet diffusion turbulent flame characteristics, such as temperature and chemical species mass fractions in a gas turbine combustion chamber. Since the neural network needs sets of examples to adapt its synaptic weights in the training phase, we used pre-assumed probability density function (PDF) method and considered chemical equilibrium chemistry model to compute the flame characteristics for generating the examples of input-output data sets. In this approach, flow and mixing field results are presented with a non-linear first order k-ε model. The turbulence model is applied in combination with preassumed β-PDF modelling for turbulence-chemistry interaction. The training algorithm for the neural network is based on a back-propagation supervised learning procedure, and the feed-forward multilayer network is incorporated as neural network architecture. The ability of ANN model to represent a highly non-linear system, such as a turbulent non-premixed flame is illustrated, and it can be summarized that the results of modelling of the combustion characteristics using ANN model are satisfactory, and the CPU-time and memory savings encouraging.

A Hybrid Model of FLANN and Firefly Algorithm for Classification

2016 ◽  
pp. 491-522 ◽  
Author(s):  
Bighnaraj Naik ◽  
Janmenjoy Nayak ◽  
H. S. Behera
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Firefly Algorithm ◽  
Statistical Significance ◽  
Optimization Techniques ◽  
Real World Data ◽  
Biologically Inspired ◽  
Functional Link ◽  
The Neural Network ◽  
Global Optima

Since last decade, biologically inspired optimization techniques have been a keen interest among the researchers of optimization community. Some of the well developed and advanced popular algorithms such as GA, PSO etc. are found to be performing well for solving large scale problems. In this chapter, a recently developed nature inspired firefly algorithm has been proposed by the combination of an efficient higher order functional link neural network for the classification of the real world data. The main advantage of firefly algorithm is to obtain the solutions for global optima, where some of the earlier developed swarm intelligence algorithms fail to do so. For learning the neural network, efficient gradient descent learning is used to optimize the weights. The proposed method is able to classify the non-linear data more efficiently with less error rate. Under null-hypothesis, the proposed method has been tested with various statistical methods to prove its statistical significance.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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