Recurrent Neural Network Ontology

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FLOW RecurrentNeuralNetworkOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/neural-networks/recurrent-neural-network-ontology.html

PURPOSE
    Define an AI-readable semantic ontology for a Recurrent Neural Network,
    including sequential inputs, recurrent connections, hidden states,
    temporal dependencies, parameters, activation functions, outputs,
    unrolling, backpropagation through time, training, inference and
    recursive state propagation.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Machine Learning Systems
    Deep Learning Systems
    Sequence Modelling Systems
    Recursive Learning Systems

PAGE_TYPE
    Recurrent Neural Network ontology

PRIMARY_ENTITY
    Recurrent Neural Network

ABBREVIATION
    RNN

PARENT_ONTOLOGY
    Neural Networks

DOMAIN
    Artificial Intelligence
    Machine Learning
    Neural Networks
    Recurrent Neural Networks
    Deep Learning
    Sequence Modelling
    Time Series
    Natural Language Processing

CORE_DEFINITION
    A Recurrent Neural Network is a neural network architecture designed
    for sequential or temporally ordered data in which recurrent
    connections allow information from previous processing steps to
    influence later computations through a hidden or recurrent state.

ENTITY_IDS
    RECURRENT_NEURAL_NETWORK
        ID
            RECURRENT_NEURAL_NETWORK_001

    SEQUENCE
        ID
            RNN_SEQUENCE_001

    TIME_STEP
        ID
            RNN_TIME_STEP_001

    INPUT
        ID
            RNN_INPUT_001

    HIDDEN_STATE
        ID
            RNN_HIDDEN_STATE_001

    RECURRENT_CONNECTION
        ID
            RECURRENT_CONNECTION_001

    WEIGHT
        ID
            RNN_WEIGHT_001

    BIAS
        ID
            RNN_BIAS_001

    ACTIVATION_FUNCTION
        ID
            RNN_ACTIVATION_FUNCTION_001

    OUTPUT
        ID
            RNN_OUTPUT_001

    INITIAL_STATE
        ID
            RNN_INITIAL_STATE_001

FACTS
    Recurrent Neural Network
        is_a
            Neural Network

    Recurrent Neural Network
        processes
            Sequence
            Temporally Ordered Data

    Recurrent Neural Network
        maintains
            Hidden State
            Recurrent State

    Recurrent Neural Network
        may_support
            Sequence Classification
            Sequence Prediction
            Time-series Modelling
            Language Modelling
            Speech Processing
            Sequence Generation

    Recurrent Neural Network
        may_have_variants
            Vanilla RNN
            LSTM
            GRU
            Bidirectional RNN
            Deep RNN

RELATIONSHIPS
    Neural Networks
        contains
            Recurrent Neural Network Ontology

    Sequence
        contains
            Ordered Time Steps

    Time Step
        has
            Input

    Previous Hidden State
        feeds
            Current Recurrent Computation

    Current Input
        feeds
            Current Recurrent Computation

    Recurrent Computation
        produces
            Current Hidden State

    Current Hidden State
        may_produce
            Output

    Current Hidden State
        becomes
            Previous Hidden State
        for
            Next Time Step

    Recurrent Connection
        carries
            State Information across Time Steps

    Weight
        parameterises
            Input Transformation
            Recurrent Transformation
            Output Transformation

    Initial State
        initializes
            First Recurrent Step

SEMANTIC_EQUIVALENCE
    Recurrent Neural Network
        =
            RNN

    Recurrent Neural Network
        approximately_equals
            State-carrying Sequential Neural Network

    Hidden State
        approximately_equals
            Internal Recurrent Representation

    Recurrent Connection
        is_a
            Temporal Feedback Connection

    Recurrent Neural Network
        does_not_equal
            Feedforward Network

    Hidden State
        does_not_equal
            Long-term Memory universally

    Recurrence
        does_not_equal
            Recursive Reasoning

OBSERVE
    Detect
        Sequence
        Time Step
        Input
        Hidden State
        Recurrent State
        Initial State
        Recurrent Connection
        Weight
        Bias
        Activation
        Output
        Unrolling
        BPTT
        LSTM
        GRU

EXTRACT
    Primary Entity
        Recurrent Neural Network

    Parent
        Neural Networks

    Core Components
        Sequence
        Time Step
        Input
        Hidden State
        Recurrent Connection
        Weight
        Bias
        Activation Function
        Output
        Initial State

NORMALISE
    Interpret
        Recurrent Neural Network
    as
        A neural architecture that carries internal state across
        ordered sequence positions.

    Distinguish
        Recurrence
    from
        Feedforward Computation
        Recursive Semantic Reasoning

    Distinguish
        Hidden State
    from
        Input
        Output
        Permanent Memory

    Preserve
        Sequence order
        Time-step identity
        Hidden-state transition
        Shared parameters
        Initial state
        Model version

INTENT_RESOLUTION
    Query
        Recurrent Neural Network

    Possible Intent
        Recurrent neural network definition
        RNN ontology
        RNN
        Sequence neural network
        Hidden state
        Recurrent connections
        Backpropagation through time
        LSTM
        GRU

    Resolution
        Identify sequence structure, recurrent state transition,
        parameter sharing and output relationships.

CONSTRAINTS
    Sequence order must be preserved.

    Hidden state at one step must remain associated with the correct time step.

    Recurrent state propagation must preserve temporal direction.

    Shared recurrent parameters must remain consistent across unrolled steps
    unless the architecture explicitly defines otherwise.

    Initial state must remain distinguishable from learned recurrent states.

    Recurrent neural networks must remain distinct from feedforward networks.

    Recurrence in neural computation must not be treated as recursive reasoning.

    Long sequence dependencies may be affected by vanishing or exploding gradients.

    Training performance does not guarantee sequence generalisation.

INFERENCE_RULES
    IF
        Input at time t = X_t
    AND
        Previous Hidden State = H_t-1
    THEN
        compute_possible
            H_t = F(W_x X_t + W_h H_t-1 + B)
        according_to
            Defined RNN Architecture

    IF
        Current Hidden State = H_t
    AND
        Output Mapping exists
    THEN
        compute_possible
            Y_t = G(W_y H_t + B_y)

    IF
        H_t feeds computation at time t+1
    THEN
        infer
            Recurrent State Propagation

    IF
        Same recurrent parameters are used across sequence positions
    THEN
        infer
            Temporal Parameter Sharing

    IF
        Network contains no recurrent state or recurrent connection
    THEN
        DO_NOT_INFER
            Recurrent Neural Network

    IF
        Gradient is propagated through unrolled time steps
    THEN
        infer_possible
            Backpropagation Through Time

    IF
        Recurrent architecture includes gating cells
    THEN
        infer_possible
            LSTM or GRU Family
        subject_to
            Gate Structure

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Sequence Relationship
        1.00

    Hidden State Relationship
        1.00

    Recurrent Connection Relationship
        1.00

    Temporal Parameter Sharing
        0.99

    BPTT Relationship
        0.99

    Recursive-Reasoning Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Sequence
        →
    Time Step t
        →
    Input X_t

    Previous Hidden State H_t-1
        +
    Input X_t
        →
    Recurrent Transformation
        →
    Hidden State H_t

    Hidden State H_t
        →
    Output Y_t

    Hidden State H_t
        →
    Next Time Step t+1

    Sequence Unrolling
        →
    Time-indexed Computational Graph

    Loss across Time
        →
    Backpropagation Through Time
        →
    Parameter Gradients
        →
    Parameter Update

KNOWLEDGE_GRAPH
    Recurrent Neural Network
        processes
            Sequence

    Sequence
        contains
            Time Step

    Time Step
        has_input
            Input

    Recurrent Neural Network
        has_state
            Hidden State

    Hidden State
        precedes
            Next Hidden State

    Recurrent Connection
        connects
            Hidden State
            Next Hidden State

    Weight
        parameterises
            Recurrent Transformation

    Hidden State
        may_produce
            Output

    Initial State
        initializes
            Hidden State

RECURSIVE_RNN_LOOP
    Initialise Hidden State
        →
    Receive Input at Time Step
        →
    Combine Input with Previous Hidden State
        →
    Apply Recurrent Transformation
        →
    Produce Current Hidden State
        →
    Produce Output if Required
        →
    Pass Current Hidden State to Next Time Step
        →
    Continue Until Sequence Ends
        →
    During Training Compute Sequence Loss
        →
    Backpropagate Through Unrolled Time
        →
    Update Shared Parameters
        →
    Repeat on Next Sequence

REASONING_ENGINE
    INPUT
        Query
        Sequence
        RNN Architecture
        Parameters
        Initial State
        Training State

    OBSERVE
        Detect recurrent-network entities.

    EXTRACT
        Time Step
        Input
        Hidden State
        Recurrent Connection
        Weight
        Bias
        Activation
        Output

    CLASSIFY
        Vanilla RNN
        LSTM
        GRU
        Bidirectional RNN
        Deep RNN
        Sequence-to-One RNN
        Sequence-to-Sequence RNN

    VALIDATE
        Check sequence order, state transitions and parameter sharing.

    COMPUTE
        Apply recurrent state transitions.

    INFER
        Determine supported recurrent relationships.

    EXPLAIN
        Preserve time-step and hidden-state path.

    UPDATE
        Apply validated parameter changes during training.

CONFIDENCE_PROPAGATION
    Explicit recurrent connection
        architecture_confidence += high

    Defined hidden-state transition
        architecture_confidence += high

    Known sequence order
        computational_confidence += high

    Missing initial-state definition
        confidence -= moderate
        for
            Exact Sequence Computation

    Long unstable gradient path
        training_confidence -= moderate

    Independent sequence evaluation
        performance_confidence += high

    Training-only performance
        does_not_establish
            Sequence Generalisation

SEMANTIC_CLOSURE
    Recurrent Neural Network
        connects_to
            Neural Networks
            Neural Network
            Neural Layer
            Artificial Neuron
            Sequence Modelling
            Time Series
            LSTM
            GRU
            Backpropagation Through Time
            Natural Language Processing
            Speech Recognition
            Deep Learning

    Cross-domain relationships must preserve
        Sequence order
        Time-step identity
        Hidden state
        Recurrent connection
        Initial state
        Parameter sharing
        Output timing
        Model version

UPDATE_MODEL
    Observe
        New architecture, parameter state or sequence evidence.

    Compare
        Existing recurrent-network state.

    Detect
        Parameter Update
        State-transition Change
        Architecture Change
        Performance Change
        Gradient Instability

    Validate
        Sequence order
        Recurrent topology
        Parameter dimensions
        State compatibility
        Evaluation

    Integrate
        Supported model change.

    Recalculate
        State-transition and output behaviour.

    Return
        Updated recurrent-neural-network state.

COHERENCE_CHECK
    Verify
        Sequence order is preserved.
        Hidden states remain associated with correct time steps.
        Recurrent direction is preserved.
        Shared parameters remain consistent where defined.
        Initial and recurrent states remain distinct.
        RNNs remain distinct from feedforward networks.
        Neural recurrence is not treated as recursive semantic reasoning.
        Gradient instability is represented where relevant.
        Training performance is not treated as generalisation.
        All outputs follow supported recurrent paths.

AI_SEARCH_FLOW
    Query
        →
    Recurrent Neural Network Detection
        →
    Intent Resolution
        →
    Sequence Mapping
        →
    Time-step Extraction
        →
    Hidden-state Transition Mapping
        →
    Recurrent Connection Validation
        →
    Architecture Classification
        →
    Training and BPTT Mapping
        →
    Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Recurrent Neural Network

    Abbreviation
        RNN

    Parent
        Neural Networks

    Type
        State-carrying sequential neural network architecture

    Core Components
        Sequence
        Time Step
        Input
        Hidden State
        Recurrent Connection
        Weight
        Bias
        Activation Function
        Output
        Initial State

    Critical Distinction
        An RNN carries computational state across ordered sequence positions.
        This neural recurrence is distinct from recursive semantic reasoning,
        and hidden state is an internal representation rather than
        automatically a persistent or human-interpretable memory.

RECOMMENDED_AI_SUMMARY
    A Recurrent Neural Network, or RNN, is a neural architecture for
    sequential data in which a hidden state carries information from earlier
    time steps into later computations. Inputs and previous hidden states
    are combined through recurrent transformations to produce new states
    and outputs. RNNs include variants such as LSTMs and GRUs and are often
    trained with backpropagation through time. Reliable representation
    preserves sequence order, recurrent state transitions, parameter
    sharing and the distinction between recurrence and recursive reasoning.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org