NeuroSolutions Feature Comparison

NeuroSolutions Features NeuroSolutions for Excel* Users Consultants Developers Topologies         Linear Regression X X X X Multilayer Perceptron (MLP) X X X X Generalized Feedforward Network X X X X Probabilistic Neural Network (PNN) X X X X Modular Network X X X Jordan / Elman Networks X X X Self-Organizing Map (SOM) X X X Principal Component Analysis (PCA) X X X Radial Basis Function (RBF) X X X General Regression Neural Network (GRNN) X X X Neuro-Fuzzy Network (CANFIS) X X X Support Vector Machine Network X X X Support Vector Machine Regression Network X X X Hopefield Network X X Time Delay Neural Network (TDNN) X X Time-Lag Recurrent Network (TLRN) X X General Recurrent Network X X Maximum Number of Inputs / Outputs / Neurons Per Layer Maximum Number of Inputs / Outputs / Neurons Per Layer indicates the number of allow inputs and outputs including the weights on each hidden-layer. 50 500 Unlimited Unlimited Maximum Number of Hidden Layers 2 6 Unlimited Unlimited Learning Paradigms         Backpropagation X X X X Unsupervised Learning Unsupervised Learning include Hebbian, Ojas, Sangers, Competitive and Kohonen. X X X Recurrent Backpropagation X X Backpropagation Through Time X X Optimization Techniques         Genetic Optimization Genetic Optimization allows you to optimize virtually any parameter in a neural network to produce the lowest error. For example, the number of hidden units, the learning rates, and the input selection can all be optimized to improve the network performance. Individual weights used in the neural network can even be updated through Genetic Optimization as an alternative to traditional training methods. X X X Input Optimization: Greedy Search Input Optimization: Greedy Search is a type of input optimization that the evolution terminates immediately when adding a single input to the previous input set does not improve the fitness. X X X Input Optimization: Back-Elimination Input Optimization: Back-Elimination is a type of input optimization that the evolution terminates when removing a single input from the previous input collection leads to a worse fitness. X X X Input Projection Component Input Projection Component reduces input dimensions by automatically mapping multiple pieces of information to single inputs. Some of the algorithms include: Principal Component Analysis, M-Dimensional Scaling, K-means Clustering, Locally Linear Embed and Self Organizing Map. X X Gradient Descent Methods         Step / Momentum X X X X Delta Bar Delta X X X X Quickprop X X X X Conjugate Gradient X X X X Levenberg-Marquardt Levenberg-Marquardt is a second-order learning algorithm that generally trains significantly faster than Momentum learning and usually arrives at a solution with a significantly lower error. It also is supported for processing through NeuroSolutions CUDA GPU processing. X X X X Advanced Features         Exemplar Weighting Exemplar Weighting improves training for data with unequal class distribution. X X X X Sensitivity Analysis Sensitivity Analysis is a technique to determine the most influential inputs. X X X X Macros / OLE Automation Macros / OLE Automation is the API to automate and control NeuroSolutions. X X X X Iterative Prediction Iterative Prediction is an advanced method for time series prediction. X X User-defined Neural Components (using DLLs) User-Defined Neural Components (using DLLs) allows you to integrate your own algorithms into NeuroSolutions through user-defined dynamic link libraries (DLLs). Every NeuroSolutions component implements a function conforming to a simple protocol in C. To add a new component you simply modify the template function for the base component and compile the code into a DLL -- all directly from NeuroSolutions! X ANSI C++ Source Code Generation ANSI C++ Source Code Generation The source code generation facility of NeuroSolutions is as robust as its object-oriented design environment. No matter how simple or complex of a network you create within the graphical user interface, NeuroSolutions will generate the equivalent neural network in ANSI C++ source code -- even those networks that contain your own algorithms implemented with DLLs! The generated network can be trained beforehand within the graphical design environment of NeuroSolutions or from within your C++ application. X

* NeuroSolutions for Excel can also be used as an add-on product to higher levels of NeuroSolutions to take advantage of the higher level features.