Synthesis and implementation of T-model neural-based A/D converter

Neural Network-Based Analog-to-Digital Converters

“…However, in practice, this concept does not work as is expected. It appears that the local minima states corrupt the correct operation of the network [14][15][16][17][18][19].…”

“…The authors in [11] proposed that the behaviour caused by the hysteresis of CMOS neurons in addition to the local minima states is a dominant contributor to the wrong network response. This hysteresis change in thresholds makes the system to stabilize at the local minima state, which is located closer to the network's present energy state at the moment of conversion [14][15][16][17][18][19]. In order to solve this problem, one of the solutions is to reset the neuron state to the initial threshold value after each conversion [11].…”

“…Thus, different methods on eliminating local minima problem are proposed in [14][15][16][17][18][19], which are discussed in more detail in Section 3.…”

“…This problem was addressed by several works that presented the ways of eliminating the local minima states by changing the structure of the ADC network [14][15][16][17][18][19]. In the following subsection, the two methods that are claimed to eliminate the problem of local minima of the energy function are presented.…”

“…In the designs by [15][16][17][18][19], the properties of the triangular connections are analyzed. In [19], the authors prove that by triangular interconnection matrix the network operates without spurious states and that this type of architecture can be a good alternative for the original Hopfield design.…”