inference system - definizione. Che cos'è inference system
Diclib.com
Dizionario ChatGPT
Inserisci una parola o una frase in qualsiasi lingua 👆
Lingua:

Traduzione e analisi delle parole tramite l'intelligenza artificiale ChatGPT

In questa pagina puoi ottenere un'analisi dettagliata di una parola o frase, prodotta utilizzando la migliore tecnologia di intelligenza artificiale fino ad oggi:

  • come viene usata la parola
  • frequenza di utilizzo
  • è usato più spesso nel discorso orale o scritto
  • opzioni di traduzione delle parole
  • esempi di utilizzo (varie frasi con traduzione)
  • etimologia

Cosa (chi) è inference system - definizione

COMPONENT OF THE SYSTEM THAT APPLIES LOGICAL RULES TO THE KNOWLEDGE BASE TO DEDUCE NEW INFORMATION
Expert system shell; Inference system; Rule-based inference engine

inference engine         
A program that infers new facts from known facts using inference rules. Commonly found as part of a Prolog interpreter, expert system or knowledge based system. (1994-11-01)
Inference engine         
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems.
Adaptive neuro fuzzy inference system         
Anfis; Adaptive Neuro Fuzzy Inference System; ANFIS; Adaptive-network-based fuzzy inference system; Adaptive network-based fuzzy inference system; Adaptive neuro-fuzzy inference system; Adaptive-neuro-fuzzy inference system
An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s.

Wikipedia

Inference engine

In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.