Learning with ease: smart neural nets

Introduces smart neural nets that learn fast with ease by regular backpropagation. This is achieved by avoiding the use of the sigmoid non-linear function driven conventional or Socratic neurons, and choosing the neurons of the hidden layers and the output layer appropriately. To develop the smart neural nets, the authors introduce what they call `the smart neurons´ and `the intelligent neurons´ that have the underpinning of `fuzzy thinking´ or `deBono thinking´. The intelligent neurons are obtained by introducing the non-emotional innovation feedback into the smart neurons. The intelligent neurons asymptotically become the same as the smart neurons. The smart neural nets are constructed by using the smart neurons and intelligent neurons. The smart neurons alone are employed to form the hidden layer (or layers) of the smart neural net. The output layer of the smart neural net is constructed by using the intelligent neurons alone. The authors compare the performance of the smart neural nets against that of the conventional neural nets toward the regular innovation backpropagation learning. Unlike the conventional neural nets, the smart neural nets seem to learn fast and smoothly by the regular innovation backpropagation learning. Further, the sigmoid non-linear function driven conventional or Socratic neurons are not essential to build feedforward neural nets. In fact, much more efficient and fast learning neural nets can be built by avoiding the conventional or Socratic neurons