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Java example source code file (ProcessModel.java)

This example Java source code file (ProcessModel.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

processmodel, realmatrix, realvector

The ProcessModel.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.filter;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;

/**
 * Defines the process dynamics model for the use with a {@link KalmanFilter}.
 *
 * @since 3.0
 */
public interface ProcessModel {
    /**
     * Returns the state transition matrix.
     *
     * @return the state transition matrix
     */
    RealMatrix getStateTransitionMatrix();

    /**
     * Returns the control matrix.
     *
     * @return the control matrix
     */
    RealMatrix getControlMatrix();

    /**
     * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
     * prediction step, so implementations of this interface may return a modified process noise
     * depending on the current iteration step.
     *
     * @return the process noise matrix
     * @see KalmanFilter#predict()
     * @see KalmanFilter#predict(double[])
     * @see KalmanFilter#predict(RealVector)
     */
    RealMatrix getProcessNoise();

    /**
     * Returns the initial state estimation vector.
     * <p>
     * <b>Note: if the return value is zero, the Kalman filter will initialize the
     * state estimation with a zero vector.
     *
     * @return the initial state estimation vector
     */
    RealVector getInitialStateEstimate();

    /**
     * Returns the initial error covariance matrix.
     * <p>
     * <b>Note: if the return value is zero, the Kalman filter will initialize the
     * error covariance with the process noise matrix.
     *
     * @return the initial error covariance matrix
     */
    RealMatrix getInitialErrorCovariance();
}

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