The aim of this project is to make the reservoir computing methods easy to use and available for .net platform without any other dependencies. Two main reservoir computing methods are called Echo State Network (ESN) and Liquid State Machine (LSM). RCNet supports both of these methods. Moreover, since ESN and LSM are based on similar general principles, RCNet allows to design complex "hybrid" recurrent reservoirs consisting of spiking and analog neurons synaptically linked together. Mutual cooperation of the hidden neurons having stateless analog and stateful spiking activation functions is enabled by specific implementation of hidden neuron. Hidden neuron is not stateless and it can fire spikes even in case of stateless analog activation is used. "Analog spikes" are based on defined firing event depending on current and previous values of the stateles activation. Hidden neuron also provides a standardized set of predictors no matter what activation function is used. According to preliminary results, it seems that it is no longer true that ESN is not capable to generalize and separate input signal enaugh to perform excellent classification. On the contrary. It now appears that the use of pure analog activations (like TanH) and the simple classical ESN reservoir design could be an "unexpected" competitor to spiking LSM reservoirs.
The main component of RCNet is called "State Machine" and it has to be instantiated through its settings class. "State Machine" is serializable so it is easily possible to instantiate and train it and than use it as a real-time loadable component in the solution.
Source code is written in C# 7.3 (.NET framework 4.7.2). More detailed documentation will be posted here as soon as the current stage of the wild changes is over.
I welcome questions, ideas and suggestions for improvements, usage experiences, bug alerts, constructive comments, etc... Please use my email address oldrich.kozelsky@email.cz to contact me.

Main functionality and possibilities of the State Machine are demonstrated in a simple demo application. Application has no startup parameters and when started, it shows the menu.
Note that if necessary, examples use Examples sub-folder relative to the location of the executable DemoConsoleApp.exe.

Application performs sequence of defined tasks. Tasks are defined in the SMDemoSettings.xml xml file, where each task is defined in the xml element "case" so you can easily insert new task or tune existing one by simple modification of xml content. SMDemoSettings.xml has to be located in the SM sub-folder relative to the location of the executable DemoConsoleApp.exe.
SMDemoSettings.xml currently also includes several classification problems from the: Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com site and State Machine usually achieves very similar results to the best classification algorithms referenced on the website.

This very simple machine learning example shows how to learn Feed Forward Network component to solve boolean algebra. Feed Forward network is a part of the State Machine's readout layer, but here is shown that it can be also used as a stand alone component.

Example shows how to manually setup State Machine configuration from the scratch, then how to train State Machine and how to verify its performance.

Several examples show usage of the State Machine Designer component to setup simple State Machine configuration, then how to train State Machine and how to verify its performance.

Input data is standardly located in the Data sub-folder relative to the location of the executable DemoConsoleApp.exe. Data is expected in csv format and data delimiter can be a tab, semicolon or comma character.

• Continuous feeding regime requires a standard csv format, where the first line contains the names of the data fields and each next line contains the data. Here is an example
• Patterned feeding regime requires specific logical csv format without colum names (header). Each data line contains values of steady (optional) and repetitive pattern features followed by expected output values at the end. Values of repetitive pattern features can be organized in two ways: groupped [v1(t1),v2(t1),v1(t2),v2(t2),v1(t3),v2(t3)] or sequential [v1(t1),v1(t2),v1(t3),v2(t1),v2(t2),v2(t3)]. Here is an example

(listed in logical order from basic to composite and complex)

See the wiki pages.

See the wiki pages.

The main component StateMachine encapsulates independent NeuralPreprocessor and ReadoutLayer components into the single component and adds support for routing specific predictors and input fields to the specific readout units. Allows to bypass NeuralPreprocessor and to use input data directly as a predictors for the readout layer.

Each component that makes up StateMachine (including StateMachine itself) has its own related settings class providing configuration, which is required by the component's constructor.
Each settings class can be instantiated manually from scratch or from a xml element encapsulating all parameters. RCNetTypes.xsd defines all xml elements used in settings classes constructors.
Each settings class also implements the GetXml method so it can be instantiated from scratch and the initialization xml element can be exported by calling the GetXml method (and stored for later use). Using xml constructors is generally preferable because the initialization xml can be edited without the need to modify source code of the manual setup.
To make things easier, RCNet also implements helper component StateMachineDesigner for easier setup of simple ESN and LSM StateMachine configurations from the code (see examples in demo application).