Concepts of Metaheuristics

The procedure used to carry out the learning process in a neural network is called the optimization algorithm (or optimizer). There are many different optimization algorithms. All have different characteristics and performance in terms of memory requirements, processing speed, and numerical precision. Four major parameters are estimated in the process of developing neural network-based models. The four significant parameters of neural networks include : 1)Activation function from Input to Hidden 2)Activation function from Hidden to Output 3)Number of hidden layers 4)The magnitude of weights of the connections (To know more about the above parameters see my tutorial on Artificial Neural Network ) This article is about the methods utilized to estimate the weights of the connections. The process of estimation of weights is similar to optimization problems. Here the weights are design variables. The transfer function prepared to transfer the information from input to output is the objectiv

Training algorithms for Neural Networks from Mrinmoy Majumder A tutorial on Conjugate Gradient Descent and Newton's Method.Go through the PPT and see if you can understand the concept and apply the same.If not do reply me.

QNANN is an algorithm which are used for update of weights of the neural networks. These algorithms are also known as training algorithm and is known to be popular enough as a technique to optimize the accuracy of neural network. In this presentation the two important techniques for weight update of neural networks at the time of training. Quasi newton artificial neural network training algorithms from Mrinmoy Majumder

Auto and Cross Correlation Coefficient is used for approximation of the auto and cross correlation of the two part of same data series and two different data series. Their magnitude depicts the way they are related to each other..Such concepts are included in the basics of statistics.However the knowledge of these two metrics are important before a model is to be developed for prediction of real time case study. You can find the tutorial by going to my slide-share account .

Can you provide me a feedback on the following tutorial ? How to optimize with the help of the Particle Swarm Optimization(PSO) Technique and xlOptimizer ? This brief tutorial will help you to solve any optimization problem with the application of Particle Swarm Optimization Method and xl addin : xlOptimizer. After a brief introduction about PSO the tutorial show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming with the help of xlOptimizer.(Some basic knowledge of MS Excel 2010 and later is required). Introduction to particle swarm optimization from Mrinmoy Majumder