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A neural network with modular hierarchical learning
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Copyright Year | 1994 |
| Description | This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion. |
| File Size | 1039971 |
| Page Count | 24 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19940024989 |
| Archival Resource Key | ark:/13960/t8jd9sn11 |
| Language | English |
| Publisher Date | 1994-03-24 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Feedforward Control Time Dependence Computer Networks Hierarchies Neural Nets Machine Learning Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Patent |
