The Current Release is Flood 2. It includes:

• A perceptron neuron model with:
• Logistic (sigmoid) activation function.
• Hyperbolic tangent (sigmoid) activation function.
• Linear activation function
• A multilayer perceptron network architecture with:
• An arbitrary number of hidden layers.
• Pre and postprocessing methods:
• Mean and standard deviation.
• Minimum and maximum.
• Independent parameters.
• Boundary conditions.
• Lower and upper bounds. 
• Several objective functionals for variational problems:
• Data modeling problems:
• Sum squared error.
• Mean squared error.
• Root mean squared error
• Minkowski error.
• Normalized squared error.
• Regularized Minkowski error.
• Examples of classical problems in the calculus of variations:
• Geodesic problem.
• Brachistochrone problem.
• Catenary problem.
• Isoperimetric problem
• Examples of optimal control problems:
• Car problem.
• Aircraft landing problem.
• Fed batch fermenter problem
• Examples of inverse problems:
• Disolution modeling of aluminium alloys
• Examples of optimal shape design:
• Minimum drag problem.
• Workaround examples for function optimization problems:
• De Jong’s function.
• Rosenbrock’s function.
• Rastrigin’s function.
• Schwefel’s function.
• Easom’s function.
• Six-hump camel back function.
• Plane-cylinder.
• Several training algorithms to optimize the objective function:
• Random search.
• Gradient descent.
• Training rate methods:
• Fixed.
• Golden section.
• Brent’s method.
• Conjugate gradient:
• Training direction methods:
• Polak-Ribiere.
• Fletcher-Reeves.
• Training rate methods:
• Fixed.
• Golden section.
• Brent’s method.
• Newton method.
• Quasi-Newton method:
• Inverse Hessian approximation methods:
• DFP.
• BFGS.
• Training rate methods:
• Fixed.
• Golden section.
• Brent’s method.
• Evolutionary algorithm:
• Fitness assignment methods:
• Linear ranking.
• Selection methods:
• Roulette wheel.
• Stochastic universal sampling.
• Recombination methods:
• Line.
• Intermediate.
• Mutation methods:
• Uniform.
• Normal.
• Different Utilities:
• Vector class.
• Matrix class.
• Input-target data set.
• Pre and postprocessing methods:
• Mean and standard deviation.
• Minimum and maximum.
• Linear regression analysis.
• Integration of functions:
• Trapezoid rule.
• Simpson’s method.
• Ordinary differential equations:
• Runge-Kutta method.
• Runge-Kutta-Fehlberg method.  

The package comes with the following documentation:

• Flood 2 InstallationGuide (PDF).

• Flood 2 User's Guide (PDF).

• Flood 2 Reference Manual (HTML).

Flood is written in ANSI C++ and should work on any compiler. So far it has been tested in Linux with gcc and in Windows with Microsoft Visual C++.