Deterministic learning of hybrid fuzzy cognitive maps and network reduction approaches
Hybrid artificial intelligence deals with the construction of intelligent systems by relying on
both human knowledge and historical data records. In this paper, we approach this problem
from a neural perspective, particularly when modeling and simulating dynamic systems.
Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to
define the interaction among the input neurons. As a second contribution, we introduce a
fast and deterministic learning rule to compute the weights among input and output neurons …
both human knowledge and historical data records. In this paper, we approach this problem
from a neural perspective, particularly when modeling and simulating dynamic systems.
Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to
define the interaction among the input neurons. As a second contribution, we introduce a
fast and deterministic learning rule to compute the weights among input and output neurons …
Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches
G NAPOLES RUIZ, A Jastrzębska, C Mosquera… - 2020 - documentserver.uhasselt.be
Hybrid artificial intelligence deals with the construction of intelligent systems by relying on
both human knowledge and historical data records. In this paper, we approach this problem
from a neural perspective, particularly when modeling and simulating dynamic systems.
Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to
define the interaction among the input neurons. As a second contribution, we introduce a
fast and deterministic learning rule to compute the weights among input and output neurons …
both human knowledge and historical data records. In this paper, we approach this problem
from a neural perspective, particularly when modeling and simulating dynamic systems.
Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to
define the interaction among the input neurons. As a second contribution, we introduce a
fast and deterministic learning rule to compute the weights among input and output neurons …
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