Year  Product  Price  Availability 
1994  AIM for DOS 1.1  $200  discontinued 
1994  AIM for Windows 2.0  $1,000  discontinued 
1995  ModelQuest (TM) / ModelQuest Prospector  $1,000  discontinued 
1996, Oct.  ModelQuest Expert  $4,000  discontinued 
1997, Apr.  ModelQuest Expert 2.0 w/StatNet Expert  $6,000  
1997, June  ModelQuest Miner  discontinued  
1997 Oct.  ModelQuest Enterprise  $60,000  
1997 Nov.  ModelQuest MarketMiner  $60,000 
Ivakhnenko made the neuron a more complex unit featuring a polynomial transfer function. The interconnections between layers of neurons were simplified, and an automatic algorithm for structure design and weight adjustment was developed.

Output of GMDH neuron 

The basic idea of GMDH adjustment is that each neuron wants to produce y at its output (i.e., the overall desired output of the network). In other words, each neuron of the polynomial network fits its output to the desired value y for each input vector x from the training set. The manner in which this approximation is accomplished is through the use of linear regression.
The training set is used to guide the process of adjusting the six weights of each neuron in the layer under construction. Each example in the training set gives one linear equation on six unknowns. Then the mean square technique is used to derive the best combination of six weights (for each neuron! plenty of matrix algebra...).
Usually, the mean square error of y' differs enormously from one neuron to another. The next step in adjusting the layer is eliminating the neurons of the layer which have an unacceptably large error. The definition of "unacceptably large" is left to the user. Certain heuristics exist to help automatic selection of the thershold. The elimination of "bad" neurons effectively reduces otherwise overwhelming combinatorial explosion of building all possible C(M_{k1}, 2) configurations.
The process of building the network continues layer by layer until a stopping criterion is satisfied. Usually, the mean square error of the best performing neuron is lower with each subsequent layer until an absolute minimum is reached. If further layers are added, the error of best performaing neuron actually rises. After the last layer is determined, each of the preceding layers undergoes anouther round of trimming to exclude those neurons that do not contribute into the final output.
The following is comparison results from here.
Neural networks  Statistical learning GMDH networks  
Data analysis  universal approximator  universal structure identificator 
Analytical model  indirect approximation  direct approximation 
Architecture  preselected unbounded network structure; experimental selection of adequate architecture demands time and experience  bounded network structure evolved during estimation process 
Network synthesis  globally optimized fixed network structure  adaptively synthesized structure 
Apriori Information  without transformation in the concepts of neural networks not usable  can be used directly to select the reference functions and criteria 
Selforganization  deductive, subjective choice of layers number and number of nodes  inductive, number of layers and of nodes estimated by minimum of external criterion (objective choice) 
Parameter estimation  in a recursive way;
demands long samples 
estimation on training set by means of maximum likelihood techniques, selection on testing set (may be extremely short or noised) 
Optimization  global search in a highly multimodal space, result depends from initial solution, tedious and requiring from user to set various algorithmic parameters by trial and error, timeconsuming technique  simultaneously optimize the structure and dependencies in model, not timeconsuming technique, inappropriate parameters not included automatically 
Access to result  available transiently in a realtime environment  usually stored and repeatedly accessible 
Initial knowledge  needs knowledge about the theory of neural networks  necessary knowledge about the kind of task (criterion) and class of system (linear,nonlinear) 
Convergence  global convergence is difficult to guarantee  model of optimal complexity is founded 
Computing  suitable for implementation using hardware with parallel computation  efficient for ordinary computers and also for massively parallel computation 
Features  generalpurpose, flexible, nonlinear (especially linear) static or dynamic models  generalpurpose, flexible linear or nonlinear, static or dynamic, parametric and nonparametric models 