Patent Document ID: 5398300
Application ID: 07814179

Base Claim:
1. A computer-implemented process for determining high and low bounds on states of output processing elements (PEs) and any hidden PEs in a continuous-state feedforward neural network additionally having input PEs and that is stored in a computer, wherein said bounds serve as limits on the possible range of each such state, said computer comprising a CPU and an input device coupled to said CPU, said CPU comprising an inference module, said neural network being fed into said CPU via said input device, said process comprising commanding the inference module to execute the steps of: determining upper and lower bounds on states of the input PEs using input variable distribution information that has been fed into said CPU via said input device; in succession, computing the bounds on the states of any hidden PEs from the bounds on the input PEs and the computed bounds on the states of any hidden PEs from hidden layers of PEs closer to the input PEs; and computing the bounds on the states of the output PEs from the bounds on the states of the input PEs and the bounds on the states of any hidden PEs.

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Claim 3:
3. A computer-implemented process for analyzing the operation of a continuous-state feedforward neural network that is stored in a computer, said computer comprising a CPU, said CPU comprising an inference module, an explanation module, and an intelligent knowledge acquisition module, said neural network being stored within the CPU, where the network includes a layer of input processing elements (PEs), a layer of output PEs, and any number of layers of hidden PEs, where the input PEs are representative of input variables and the output PEs are representative of output variables, said process comprising commanding the inference module to perform the steps of: determining a tentative decision for each output variable based upon states of the output PEs in the neural network; determining a final conclusion for each output variable using the process of claim 1 where, as values of input variables become known, the tentative decision eventually becomes irreversible; and computing confidence measures for each tentative decision and final conclusion; commanding the explanation module to perform the steps of: explaining each tentative decision by computing the contribution made towards the decision by each input variable whose value is known; explaining each final conclusion by computing the contribution made towards reaching the conclusion by each input variable whose value is known; and converting knowledge implicit in the neural network into an explicit set of if-then rules; and commanding the intelligent knowledge acquisition module to perform the step of determining potential influence on a tentative decision by each input variable whose value is unknown.