INTRODUCTION ------------ Thanks for trying the shareware version of QwikNet. QwikNet is a powerful, yet easy to use artificial neural network simulator. QwikNet implements several different algorithms to train a standard feed-forward neural network in an easy-to-use graphical environment. The shareware version is limited to a maximum of 1 hidden layer and 10 hidden neurons. The registered version can accommodate up to 5 hidden layers with no limits (besides hardware) on the number of hidden neurons. You may evaluate this software free for 30 days, after which you must either purchase the registered version or delete the software from your machine. Please feel free to distribute the file QwikNet.ZIP to friends, associates, or to a computer bulletin board system (BBS). REGISTRATION ------------ The registration fee is $40 US. Please print and fill out the registration form in the file 'register.txt', (also found in the help file), and enclose it with your payment. Upon receipt of payment you will receive the full registered version and all future upgrades. INSTALLATION ------------ The 32-bit version, QwkNet32.EXE, requires a minimum 486 processor and Windows 95 or NT. This version removes all practical limits on network/data file size. Installation of both versions, including the DLLs and help files, requires less than 1.5 MB of disk space. 32 bit version - copy the following files to your directory: QwkNet32.EXE - 32 bit executable for Win95 or NT BWCC32.DLL - 32-bit DLL needed by QwkNet32 QwikNet.HLP - Windows help file README.TXT - This file REGISTER.TXT - A registration form in ASCII format FILE_ID.DIZ - Information file for bulletin boards Example data files - optional EXAMPLE DATA FILES ------------------ Several example data files are included for testing: SINCOS.TRN - Training data file with one input (x) and two outputs Sin(x) and cos(x). SINCOS.TST - Testing data for SINCOS. SINCOS.WTS - Example of saved weights. SINCOS.NET - SINCOS project file. SECURITY.* - A more complex data set for Power System Security analysis. The data set contains 33 inputs (power system features) and 1 output (security classification). XOR.* - XOR training data set. Two inputs and 1 output. QUESTIONS/COMMENTS ------------------ Please send any questions, comments or bug reports to: Craig Jensen 9935 NE 125th Ln #4 Kirkland, WA 98034 email - cjensen@ee.washington.edu