7.3 Characteristics of High-Performance DAMs

Based on the above analysis and comparison of DAM retrieval performance, a set of desirable performance characteristics can be identified. Figures 7.3.1 (a) and (b) present a conceptual diagram of the state space for high- and low-performance DAMs, respectively (Hassoun, 1993).

Figure 7.3.1. A conceptual diagram comparing the state space of (a) high-performance and (b) low-performance autoassociative DAMs.

The high-performance DAM in Figure 7.3.1(a) has large basins of attraction around all fundamental memories. It has a relatively small number of spurious memories, and each spurious memory has a very small basin of attraction. This DAM is stable in the sense that it exhibits no oscillations. The shaded background in this figure represents the region of state space for which the DAM converges to a unique ground state (e.g., zero state). This ground state acts as a default "no decision" attractor state where unfamiliar or highly corrupted initial states converge to this default state.

A low performance DAM has one or more of the characteristics depicted conceptually in Figure 7.3.1 b. It is characterized by its inability to store all desired memories as fixed points; those memories which are stored successfully end up having small basins of attraction. The number of spurious memories is very high for such a DAM, and they have relatively large basins of attraction. This low performance DAM may also exhibit oscillations. Here, an initial state close to one of the stored memories has a significant chance of converging to a spurious memory or to a limit cycle.

To summarize, high-performance DAMs must have the following characteristics (Hassoun and Youssef, 1989): (1) High capacity. (2) Tolerance to noisy and partial inputs. This implies that fundamental memories have large basins of attraction. (3) The existence of only relatively few spurious memories and few or no limit cycles with negligible size of basins of attraction. (4) Provision for a "no decision" default memory/state; inputs with very low "signal-to-noise" ratios are mapped (with high probability) to this default memory. (5) Fast memory retrievals. This list of high-performance DAM characteristics can act as performance criteria for comparing various DAM architectures and/or DAM recording recipes.

The capacity and performance of DAMs can be improved by employing optimal recording recipes (such as the projection recipe) and/or using proper state updating schemes (such as serial updating) as was seen in Section 7.2. Yet, one may also improve the capacity and performance of DAMs by modifying their basic architecture or components. Such improved DAMs and other common DAM models are presented in the next section.

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