Earlier it was stated that simulation and machine learning are almost opposites. By this, we are talking about inputs and the model. With simulation, the random variable inputs aren't known exactly, but the model is often known exactly. With machine learning, the inputs are known exactly, but the model is unknown prior to training. Regarding output, the differences are more subtle.

In this article we are going to discuss training neural networks using particle swarm optimization (PSO). Training a neural network is an optimization problem so the optimization algorithm is of primary importance. When training MLPs we are adjusting weights between neurons using an error function as our optimization objective. PNNs and GRNNs use a smoothing factor, σ to define the network. The objective is to find sigmas that minimize error.

Vaimal is a machine learning add-in that allows you to train and deploy machine learning algorithms without programming. You can make predictions on new data using models that are trained on historical data. Vaimal allows you to create decision trees, support vector machines and neural networks all within Excel . It also includes more powerful ensemble methods to combine models for even better predictive performance. The easy to use interface allows you to focus on your data without worrying about learning mundane programming tasks required with common machine learning platforms.