This research presents the optimization of radial basis function (RBF) neural network by means of aFOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The form of amended fruit fly optimization algorithm (aFOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. The validity of model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical.
Our aim is to find a variable function based on such a large number of experimental data in many scientific experiments such as Near Infrared Spectral data and Atlas data. But this kind of function is often highly uncertain, nonlinear dynamic model. When we perform on the data regression analysis, this requires choosing appropriate independent variables to establish the independent variables on the dependent variables regression model. Generally, experiments often get more variables, some variables affecting the results may be smaller or no influence at all, even some variable acquisition need to pay a large cost. If drawing unimportant variables into model, we can reduce the precision of the model, but cannot reach the ideal result. At the same time, a large number of variables may also exist in multicollinearity. Therefore, the independent variable screening before modeling is very necessary. Because the fruit fly optimization algorithm has concise form, is easy to learn, and have fault tolerant ability, besides algorithm realizes time shorter, and the iterative optimization is difficult to fall into the local extreme value. And radiate basis function (RBF) neural network’s structure is simple, training concise and fasting speed of convergence by learning, can approximate any nonlinear function, having a "local perception field" reputation. For this reason, this paper puts forward a method of making use of the amended fruit flies optimization algorithm to optimize RBF neural network (aFOA-RBF algorithm) using for variable selection.
Table of Contents
1. Introduction
1.1 Introduction
1.2 Motivation and Aim
1.3 Disposition
2. Artificial Neural Network
2.1 Biological Neuron
2.2 Artificial Neural Network
2.2.1 Single Layer Perceptron
2.2.2 Multi Layer Perceptron
2.3 Artificial Neuron and Activation Function
2.3.1 Linear Activation Function
2.3.2 Non Linear Activation Functions
2.3.3 Radial Basis Net
2.4 Learning Methods
2.4.1 Supervised Learning
2.4.2 Unsupervised learning
2.5 Neural Network Applications
2.5.1 Process Control
2.5.2 Speech Recognition
2.5.3 Image Compression
2.5.4 Medical Imaging
2.5.5 Image Processing
2.5.6 Face Recognition
2.5.7 Road and Obstacle Recognition
3. Neural Network Parallelism
3.1 General Aspects of Parallel Processing
3.2 Back Propagation Neural Network Parallelism
3.3 Neural Network Distribution
3.4 Distributed computing for BP parallelism
3.4.1 Training Set Parallelism
3.4.2 Node Parallelism
3.4.2.1 Neuron Parallelism
3.4.2.2 Synapse Parallelism
3.5 Need of Parallel Processing
4. Distributed Computing System
4.1 Distributed System
4.2 Distributed Computing System Models
4.2.1 Minicomputer Model
4.2.2 Workstation Model
4.2.3 Workstation-server Model
4.2.4 Processor Pool Model
4.2.5 Hybrid Model
4.3 Distributed Computing Environment
5. Fruit Fly
5.1 Fruit Fly
5.1.1 Fruit Fly Nervous System: Solution to Computer Problem
5.1.2 Fruit Fly takes Distributive Approach in Computing
5.2 Fruit Fly Revolutionize distributed computing
5.3 Fruit Fly Optimization
6. Literature Survey
6.1 Literature Review
7. Present Works
7.1 Overview
7.2 aFOA Algorithm
7.3 Radial Basis Neural Network
7.4 Variable Selection
7.5 Model Development
7.5.1 Data Preparation
7.5.2 Constructing Model
8. Framework and Technology
8.1 MATLAB
8.2 MemBrain
8.3 System and Models
8.3.1 System Requirement
8.3.2 MemBrain Mathematical Model
8.3.3 Learning algorithms
8.4 MemBrain DLL
8.5 Automatic Code Generation
9. Result and Discussion
9.1 Experimental results and analysis
9.2 Numerical Testing
9.3 Comparison
10. Conclusion
10.1 Conclusion
10.2 Challenge and Future Work
Research Objectives and Core Themes
This research aims to optimize the Radial Basis Function (RBF) neural network by utilizing the amended Fruit Fly Optimization Algorithm (aFOA) in combination with the Mean Impact Value (MIV) for effective variable selection to improve computational performance and modeling accuracy.
- Optimization of RBF neural networks using aFOA.
- Application of Mean Impact Value (MIV) for input variable screening.
- Parallel processing and distributed computing architectures for neural networks.
- Performance evaluation through experimental data and comparative analysis with existing optimization algorithms.
Excerpt from the Book
5.1.1 Fruit Fly Nervous System: Solution to Computer Problem
The fruit fly has evolved a method for arranging the tiny, hair-like structures it uses to feel and hear the world that's so efficient a team of scientists in Israel and at Carnegie Mellon University says it could be used to more effectively deploy wireless sensor networks and other distributed computing applications. With a minimum of communication and without advance knowledge of how they are connected with each other, the cells in the fly's developing nervous system manage to organize themselves so that a small number of cells serve as leaders that provide direct connections with every other nerve cell, said author Ziv Bar-Joseph, associate professor of machine learning and computational biology at Carnegie Mellon University.
The result, the researchers report in the Jan. 14 edition of the journal Science, is the same sort of scheme used to manage the distributed computer networks that perform such everyday tasks as searching the Web or controlling an airplane in flight. But the method used by the fly's nervous system to organize itself is much simpler and more robust than anything humans have concocted. "It is such a simple and intuitive solution, I can't believe we did not think of this 25 years ago," said co-author NogaAlon, a mathematician and computer scientist at Tel Aviv University and the Institute for Advanced Study in Princeton, N.J. Bar-Joseph, Alon and their co-authors — Yehuda Afek of Tel Aviv University and NaamaBarkai, EranHornstein and OmerBarad of the Weizmann Institute of Science in Rehovot, Israel — used the insights gained from fruit flies to design a new distributed computing algorithm.
They found it has qualities that make it particularly well suited for networks in which the number and position of the nodes is not completely certain. These include wireless sensor networks, such as environmental monitoring, where sensors are dispersed in a lake or waterway, or systems for controlling swarms of robots. "Computational and mathematical models have long been used by scientists to analyze biological systems," said Bar-Joseph, a faculty member of the Lane Center for Computational Biology and the Machine Learning Department in Carnegie Mellon's School of Computer Science. "Here we've reversed the strategy, studying a biological system to solve a long-standing computer science problem."
Summary of Chapters
Chapter 1 Introduction: Provides an overview of the thesis, outlining the motivation for using parallel-distributed neural networks and the objectives of the research.
Chapter 2 Artificial Neural Network: Details the fundamental concepts of biological neurons and various artificial neural network architectures, including single and multi-layer perceptrons.
Chapter 3 Neural Network Parallelism: Explains the necessity and implementation of parallel processing techniques for back-propagation in neural networks.
Chapter 4 Distributed Computing System: Discusses various models of distributed computing, ranging from workstation models to processor-pool architectures.
Chapter 5 Fruit Fly: Investigates the biological mechanisms of fruit flies and how their distributive behavior can be applied to solve complex computational problems.
Chapter 6 Literature Survey: Reviews existing research and literature concerning the application of fruit fly optimization algorithms in various scheduling and computational domains.
Chapter 7 Present Works: Describes the methodology for optimizing RBF neural networks using aFOA and the MIV-based variable selection process.
Chapter 8 Framework and Technology: Explains the software tools, specifically MATLAB and MemBrain, used for implementing and analyzing the proposed algorithms.
Chapter 9 Result and Discussion: Presents the experimental results, numerical testing, and performance comparisons of the proposed algorithm.
Chapter 10 Conclusion: Summarizes the major contributions of the research and identifies potential future work and challenges in the field.
Keywords
Radial Basis Function (RBF), Neural Network, Fruit Fly Optimization Algorithm (aFOA), Mean Impact Value (MIV), Variable Selection, Distributed Computing, Back-Propagation, Parallel Processing, Swarm Intelligence, Optimization, Modeling, Simulation, MATLAB, MemBrain, Set-Streaming Stream-Shop Scheduling.
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on the optimization of Radial Basis Function (RBF) neural networks using an amended Fruit Fly Optimization Algorithm (aFOA) to enhance training efficiency and variable selection accuracy.
What are the central thematic areas covered?
The work integrates concepts from biological inspiration (fruit flies), distributed computing systems, artificial neural network architectures, and swarm intelligence-based optimization techniques.
What is the main objective of the thesis?
The primary objective is to develop a more efficient model for RBF neural networks that can perform robust variable selection, thereby reducing computational costs and improving the prediction precision of complex, nonlinear systems.
Which scientific methodology is employed?
The researcher uses an amended Fruit Fly Optimization Algorithm (aFOA) combined with the Mean Impact Value (MIV) evaluation method to optimize the network structure and screen influential independent variables.
What does the main body of the work cover?
The main body covers theoretical foundations of neural networks, distributed systems architecture, the logic behind the fruit fly algorithm, and experimental validation using data sets to demonstrate the effectiveness of the aFOA-RBF approach.
Which keywords characterize this work?
Key terms include RBF Neural Networks, aFOA, MIV, Variable Selection, Distributed Computing, Swarm Intelligence, and Set-streaming Stream-shop Scheduling.
How does the fruit fly behavior inform the optimization process?
The algorithm mimics the foraging behavior of fruit flies, specifically their ability to sense food sources using osphresis and vision, to navigate the search space and find global optima more effectively than traditional methods.
What role does the MemBrain simulator play in this research?
MemBrain serves as a flexible graphical editor and simulator for neural networks, allowing the author to design, train, and test the proposed RBF network architectures and generate C-code for production implementation.
- Citar trabajo
- M. Tech. CSE Anurag Rana (Autor), 2014, Radial basis neural network optimization using fruit fly, Múnich, GRIN Verlag, https://www.grin.com/document/275287
